The conventional drug discovery and development process has beenassociated with high expenditure, long durations and low success rate, andtherefore new methods are needed. Machine Learning (ML) and ArtificialIntelligence (AI) are proving to be revolutionary measures offering a greatimprovement of efficiency, accuracy and innovation as a part of thepharmaceutical pipeline. This review is an investigation of the remarkablealteration of AI/ML, starting with the recognition and confirmation of thetargets, and proceeding with complex molecular docking, de novo drugdesign, correct ADMET (Absorption, Distribution, Metabolism, Excretion,Toxicity) prediction. Such AI-based approaches have shown phenomenaltrends, such as 80-90% success rates in Phase I clinical trials and up to 70%cost reduction in development timelines reduced to less than 10 years andpossibly 3-6 years. In addition, both similar and different methods in clinicaltrial optimization using AI have a history of high-quality patient recruitment,predictive modeling, adaptive designs, and the availability of digital twinsthat open precision medicine. Nonetheless, there are major challenges evendespite these opportunities. These involve important data related barrierswith regard quality, quantity, diversity, privacy and security. Many AImodels are considered as the black box, which results in difficulties withinterpretability and explainability, which in turn prevents regulatoryacceptance and trust. They are also heavily burdened by large computationaldemands, smooth combination with experimental methods, and regulatory,ethical and intellectual property issues, which are developing. Future trendsare more complex algorithms such as Generative AI and QuantumComputing, new data sharing methods such as federated learning, as well asbetter integration into more advanced experimental platforms such as Organon-a-Chip technologies. Future AI use implies that we would achieve thefull potential of AI in designing safer, more effective, and accessiblemedicines in case of the stable innovation, thorough validation, transparentgovernance, and effective collaboration across disciplines.