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Pharmaceutical Product Optimization Using Artificial Intelligence and Machine Learning: A Comprehensive Bibliometric Analysis

Author

Listed:
  • Sabrine Khemiri

    (Higher Institute of Management of Tunis, Université de Tunis, Lab SMART)

  • Said Gattoufi

    (Higher Institute of Management of Tunis, Université de Tunis, Lab SMART)

Abstract

The pharmaceutical sector is undergoing continual transformation driven by the desire to enhance medicine quality, cut production costs, and improve operational efficiency. In this context, artificial intelligence (AI), machine learning (ML), and advanced optimization algorithms have become increasingly significant in pharmaceutical research. This paper includes a complete bibliometric analysis of scholarly articles concentrating on the use of AI, ML, and computational optimization in pharmaceutical product development and process improvement. Following the PRISMA technique, we extracted and screened papers from the Scopus database for the period 2020–2025, resulting in a final dataset of 2,579 relevant documents. The analysis highlights major research trends, influential authors, top journals, commonly used techniques, developing themes, and global collaboration patterns. Results indicate a strong growth in AI-driven pharmaceutical research, particularly in drug discovery, drug design, formulation optimization, predictive modeling, and real-time process control. This bibliometric analysis presents an evidence-based overview of the scientific landscape and outlines future research areas for intelligent pharmaceutical product improvement.

Suggested Citation

  • Sabrine Khemiri & Said Gattoufi, 2026. "Pharmaceutical Product Optimization Using Artificial Intelligence and Machine Learning: A Comprehensive Bibliometric Analysis," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-23493-3_6
    DOI: 10.1007/978-3-032-23493-3_6
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