AI and Machine Learning in Pharmaceutical Manufacturing: Revolutionizing Process Optimization

Authors

  • Ashish Gorle Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405. https://orcid.org/0000-0002-7392-2204
  • Shruti Pawar Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.
  • Arak Dipali Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.
  • Hritik Nimbarthe Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.
  • Chaudhary Leena Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Abstract

The pharmaceutical manufacturing industry faces increasing pressure to enhance operational efficiency, maintain high product quality, and meet stringent regulatory requirements. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of optimizing various facets of pharmaceutical production. From predictive maintenance and real-time quality control to process optimization, these advanced techniques are reshaping how pharmaceutical companies approach production processes. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly being employed to analyse large volumes of data generated throughout the pharmaceutical manufacturing lifecycle. The integration of AI in the pharmaceutical industry marks a significant advancement, offering a multitude of benefits while addressing the complexities and challenges of modern healthcare and drug research. This review article provides an overview of the historical evolution, goals, and applications of AI and ML in pharmaceutical manufacturing. It also explores the various benefits and challenges associated with their implementation, highlighting case studies, exploring their role in improving process design, predictive maintenance, quality control, supply chain management, regulatory compliance and the future prospects of these technologies in revolutionizing the pharmaceutical industry.

Keywords: Artificial Intelligence, Machine Learning, pharmaceutical manufacturing

Keywords:

Artificial Intelligence, Machine Learning, pharmaceutical manufacturing

DOI

https://doi.org/10.22270/jddt.v15i10.7422

Author Biographies

Ashish Gorle , Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Shruti Pawar, Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Arak Dipali, Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Hritik Nimbarthe, Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Chaudhary Leena, Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

Dept. Pharmaceutical Technology, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Tal. Shirpur, Dist. Dhule, Maharashtra, India, 425 405.

References

1. Jiang J, Xyangyu Ma, Ouyang D, Robort O. Williams III, Emerging artificial intelligence (AI) technologies are used in the development of solid dosage forms. Pharmaceutics. 2022; 14(11), 2257 https://doi.org/10.3390/pharmaceutics14112257 PMid:36365076 PMCid:PMC9694557

2. Sheikh H, Prins C, Schrijvers E, Artificial Intelligence: Definition and Background. In: Mission AI. Research for Policy. Publisher Springer, Cham, 2023, https://doi.org/10.1007/978-3-031-21448-6_2 https://doi.org/10.1007/978-3-031-21448-6_2

3. Chen Y, Wang H, Yu K, Zhou R, Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review, Highlights in Science Engineering and Technology, 2024; 85:545-550 https://doi.org/10.54097/vfwgas09

4. Patel V, Shah M, Artificial intelligence and machine learning in drug discovery and development. Intelligent Medicine. 2022; 2(3):134-140 https://doi.org/10.1016/j.imed.2021.10.001

5. Blanco-Gonzalez A, Alfonso Cabezon A, Seco-Gonzalez A, Conde-Torres, Antelo-Riveiro P, Angel Pineiro A, Rebeca Garcia-Fandino R, The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies, Pharmaceuticals MDPI, 2023; 16(6):891 https://doi.org/10.3390/ph16060891 PMid:37375838 PMCid:PMC10302890

6. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK, Artificial intelligence in drug discovery and development, Drug Discovery. Today, 2021; 26:80-93 https://doi.org/10.1016/j.drudis.2020.10.010 PMid:33099022 PMCid:PMC7577280

7. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Artificial intelligence: A powerful paradigm for scientific research. Innovation. 2021; 2:100179. https://doi.org/10.1016/j.xinn.2021.100179 PMid:34877560 PMCid:PMC8633405

8. Wirtz B.W. Artificial intelligence and the public sector-applications and challenges, International Journal of Public Administration, 2019; 42:596-615. https://doi.org/10.1080/01900692.2018.1498103

9. Mak K.-K., Pichika M.R. Artificial intelligence in drug development: present status and future prospects, Drug Discovery Today, 2019; 24:773-780 https://doi.org/10.1016/j.drudis.2018.11.014 PMid:30472429

10. Singh R, Arya P, Dubey SH, Artificial Intelligence in Pharmaceutics: Revolutionizing Drug Formulation and Optimization, Journal of Drug Discovery and Health Sciences, 2024; 1(03):138-145 https://doi.org/10.21590/jddhs.01.03.03

11. Saha GC, Eni LN, Saha H, Parida PK, Rathinavelu R, Jain SK, Haldar B, Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making, Rivista Italiana di Filosofia Analitica Junior, 2023; 14(2):116-126

12. Khalid S, Optimization of Pharmaceutical Processes Using Artificial Intelligence, 2025; 5(1):1134-44 https://doi.org/10.18535/cmhrj.v5i1.447

13. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P, Molecular Diversity, 2021; 25(3):1315-1360 https://doi.org/10.1007/s11030-021-10217-3 PMid:33844136 PMCid:PMC8040371

14. Ali KA, Mohin S, Mondal P, Goswami S, Ghosh S, Choudhuri S. Influence of artificial intelligence in modern pharmaceutical formulation and drug development, Future Journal of Pharmaceutical Sciences, 2024;10(1):53 https://doi.org/10.1186/s43094-024-00625-1

15. Zhu H. Big data and artificial intelligence modelling for drug discovery, The Annual Review of Pharmacology and Toxicology, 2020; 60:573-589 https://doi.org/10.1146/annurev-pharmtox-010919-023324 PMid:31518513 PMCid:PMC7010403

16. Mundhra S, Kadiri SK, Tiwari P, Harnessing AI and Machine Learning in Pharmaceutical Quality Assurance, Journal of Pharmaceutical Quality Assurance and Quality Control, 2024; 6(1):19-29

17. Curate B, AI: optimizing personalized medicine with artificial intelligence, SLAS Technology, 2020; 25:95-105 https://doi.org/10.1177/2472630319890316 PMid:31771394

18. Rishi R Gupta, Application of Artificial Intelligence and Machine Learning in Drug Discovery, Methods in Molecular Biology, 2022; 2390:113-124 https://doi.org/10.1007/978-1-0716-1787-8_4 PMid:34731466

19. Chan HS, Advancing drug discovery via artificial intelligence, Trends in Pharmacological Sciences 2019; 40(8):592-604 https://doi.org/10.1016/j.tips.2019.06.004 PMid:31320117

20. Duch W, Artificial intelligence approaches for rational drug design and discovery, Current Pharmaceutical Design, 2007; 13:1497-1508 https://doi.org/10.2174/138161207780765954 PMid:17504169

21. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade R, Artificial intelligence in drug discovery and development, Drug Discovery Today, 2020; 21;26(1):80-93 https://doi.org/10.1016/j.drudis.2020.10.010 PMid:33099022 PMCid:PMC7577280

Published

2025-10-15
Statistics
Abstract Display: 179
PDF Downloads: 192
PDF Downloads: 31

How to Cite

1.
Gorle A, Pawar S, Dipali A, Nimbarthe H, Leena C. AI and Machine Learning in Pharmaceutical Manufacturing: Revolutionizing Process Optimization. J. Drug Delivery Ther. [Internet]. 2025 Oct. 15 [cited 2025 Nov. 10];15(10):195-200. Available from: https://jddtonline.info/index.php/jddt/article/view/7422

Issue

Vol. 15 No. 10 (2025): Volume 15, Issue 10, October 2025

Section

Review

Copyright (c) 2025 Ashish Gorle , Shruti Pawar, Arak Dipali, Hritik Nimbarthe, Chaudhary Leena

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

How to Cite

1.
Gorle A, Pawar S, Dipali A, Nimbarthe H, Leena C. AI and Machine Learning in Pharmaceutical Manufacturing: Revolutionizing Process Optimization. J. Drug Delivery Ther. [Internet]. 2025 Oct. 15 [cited 2025 Nov. 10];15(10):195-200. Available from: https://jddtonline.info/index.php/jddt/article/view/7422
Crossref
Scopus
Google Scholar
Europe PMC