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 challenges 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 review calls for continued research, collaboration, and dialogue among stakeholders to fully realize the transformative potential of ML in drug discovery.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 challenges 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 review calls for continued research, collaboration, and dialogue among stakeholders to fully realize the transformative potential of ML in drug discovery.