The practice of discovering novel therapeutic applications for pharmaceuticals that have previously received approval, known as "drug repurposing," has drawn a lot of interest since it may cut down on the time and expenses related to new drug development. Drug repurposing provides a more effective alternative to traditional drug discovery methods, which are sometimes costly and time-consuming, by making use of de-risked molecules. The emergence of machine learning (ML) has brought about even more revolutionary changes in this industry. This study critically investigates how machine learning (ML) affects medication repurposing, emphasizing how ML speeds up the drug development process, enhances the interpretation of intricate biological data, and provides more affordable options than traditional approaches. This paper analyzes the advances in algorithms like multitask learning, supervised learning, and deep learning, and their roles in accelerating target identification and drug development through a detailed investigation of current machine learning applications. The report also covers the financial advantages of machine learning (ML) in medication repurposing, emphasizing how it may save research expenses and boost productivity by automating data processing and utilizing current datasets. The research adds to the ongoing conversation about the future of drug discovery and the critical role that machine learning will play in improving the efficacy and efficiency of repurposing existing medications. While highlighting the transformative potential of machine learning (ML) in the pharmaceutical industry, particularly in drug repurposing, the study also acknowledges the challenges that come with integrating ML into the drug development pipeline. These challenges include the need for significant validation through experiments and addressing issues related to data integration.