The use of artificial intelligence (AI) in drug discovery has transformed the medical field by substantially shortening up the process of identifying possible medicinal molecules. In-silico validation, which uses computational approaches to predict the efficacy, safety, and mechanisms of action of drug candidates, has become a critical tool in the early phases of drug development. This strategy combines AI algorithms with molecular modeling, docking, and machine learning approaches to mimic drug-target interactions, allowing potential candidates to be identified prior to experimental testing. AI-assisted drug discovery uses large datasets from biological, chemical, and clinical sources to train models capable of predicting therapeutic efficacy, toxicity, and off-target interactions. In-silico validation minimizes the time and cost of standard drug development approaches while boosting the accuracy and dependability of outcomes. This research investigates the role of AI-assisted in-silico approaches in evaluating medication candidates for a variety of disorders, as well as their potential applications in personalized medicine. Furthermore, it emphasizes AI’s ability to solve difficulties such as the complexities of human biology, high drug candidate attrition rates, and the need for more efficient and cost-effective healthcare solutions. Despite limitations in AI model generalization and the need for extensive clinical validation, AI-assisted in-silico methodologies show enormous potential for revolutionizing the future of drug development and healthcare delivery.