Modern society has placed more emphasis on well-being, focusing on better healthcare services at whatever cost. In the same vein, the healthcare system is working towards extending population health, improving treatment efficiency, and incrementally enhancing the experiences of patients. However, gaining and making sense of various complex biomedical data has been hard to get in the process of reforming healthcare. Modern biomedical research combines all types of information, from imaging to electronic health records, text, and sensor data, characterized by complexity, heterogeneity, and often ambiguity. Conventional statistical learning and data mining methods need a tremendous amount of feature engineering to extract meaningful insights and then build predictive or clustering models. However, these approaches are badly impeded due to the complex nature of the data and the lack of domain expertise. Deep learning is a disruptive technology that sidesteps conventional feature engineering and enables end-to-end learning directly from complex clinical data. Advanced architectures formulate a paramount data analysis at unprecedented scales and complexities, foretelling rapid, efficient, and precise insights. Deep learning integrated into health care holds several advantages in decision-making, mimicking human cognition. The multiple-layer architectures enable superior computational capabilities and refine vast amounts of previously untapped healthcare data. A capability like this would democratize expertise and let all health practitioners work right on the front lines while performing at a top specialist level. The sharing of deep learning models across different healthcare institutions could be done without the fear of leaking patient data, and this will open the way for a new generation of personalized medicine. However, the interpretability of models and other ethical concerns remain pertinent challenges. This review underlines deep learning as having a transformative role in health care by underscoring that it will help harness the vast biomedical data for the betterment of human health.