This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges. By addressing these hurdles and leveraging the capabilities of ML, the drug discovery process can be significantly accelerated, paving the way for the development of new therapeutics. The potential of ML to revolutionize drug discovery lies not only in its efficiency and predictive capabilities but also in its capacity to provide novel insights into the biological mechanisms underlying diseases. By analyzing large-scale biological datasets, such as genomic, proteomic, and metabolomic data, ML algorithms can identify new drug targets and propose novel therapeutic strategies that were previously unattainable with traditional methods. Furthermore, ML can enhance the precision medicine approach by predicting individual responses to drugs based on genetic makeup, thereby improving treatment outcomes and reducing adverse effects. This review aims to critically examine the applications of ML in the drug discovery process, highlighting the significant advancements it has brought to various stages of drug development. Equally important, this review aims to identify the challenges and limitations faced when integrating ML into drug discovery. These challenges include data quality and availability issues, the interpretability of ML models, and the need for effective integration of ML tools into existing drug discovery workflows. By addressing these challenges and exploring potential solutions, this review provides a comprehensive overview of the current state of ML in drug discovery, offering insights into its future direction and the continued efforts required to fully realize its potential. Through this examination, we aim to contribute to the ongoing dialogue among researchers, practitioners, and policymakers on leveraging ML to accelerate the development of new, effective therapeutic agents.This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges. By addressing these hurdles and leveraging the capabilities of ML, the drug discovery process can be significantly accelerated, paving the way for the development of new therapeutics. The potential of ML to revolutionize drug discovery lies not only in its efficiency and predictive capabilities but also in its capacity to provide novel insights into the biological mechanisms underlying diseases. By analyzing large-scale biological datasets, such as genomic, proteomic, and metabolomic data, ML algorithms can identify new drug targets and propose novel therapeutic strategies that were previously unattainable with traditional methods. Furthermore, ML can enhance the precision medicine approach by predicting individual responses to drugs based on genetic makeup, thereby improving treatment outcomes and reducing adverse effects. This review aims to critically examine the applications of ML in the drug discovery process, highlighting the significant advancements it has brought to various stages of drug development. Equally important, this review aims to identify the challenges and limitations faced when integrating ML into drug discovery. These challenges include data quality and availability issues, the interpretability of ML models, and the need for effective integration of ML tools into existing drug discovery workflows. By addressing these challenges and exploring potential solutions, this review provides a comprehensive overview of the current state of ML in drug discovery, offering insights into its future direction and the continued efforts required to fully realize its potential. Through this examination, we aim to contribute to the ongoing dialogue among researchers, practitioners, and policymakers on leveraging ML to accelerate the development of new, effective therapeutic agents.