Artificial Intelligence (AI) and Machine Learning (ML) have moved from being peripheral computational curiosities to becoming central tools in modern pharmaceutical science. From the discovery of a new chemical entity to the monitoring of a tablet-compression line on the shop floor, data-driven models now inform decisions that were once based purely on experience and manual experimentation. This book has been written to serve as a companion text for the course AI Applications in Pharmaceutical Sciences — offered to B.Pharm students. It is designed to build conceptual clarity rather than to train students as programmers. The emphasis throughout is on understanding what a model does, why it is used, what data it needs, and — just as importantly — where its limitations lie. The book follows the five units of the prescribed syllabus: AI in Natural Products and Pharmacognosy, AI in Medicinal and Pharmaceutical Chemistry, AI in Drug Product Design and Performance Prediction, AI in Process Monitoring and Production Systems, and AI in Pharmaceutical Analysis and Chemometrics. Each unit opens with a short overview, proceeds through every topic listed in the syllabus with explanations and pharmaceutical examples, and closes with a summary and self-assessment questions to reinforce learning. It is hoped that this book will help students appreciate AI not as a replacement for pharmaceutical judgement, but as a powerful assistant that extends the reach of the pharmaceutical scientist — provided it is used with an understanding of its assumptions, its data requirements, and its boundaries.