This survey paper provides a thorough examination of the application of deep learning methodologies in disease prediction. It critically analyses and compares existing studies utilizing these innovative techniques across a spectrum of diseases. Specific emphasis is laid on the types of deep learning models employed, the diversity of datasets utilized, and the overall predictive performance achieved. The paper underscores the immense potential of deep learning technologies in enhancing prediction accuracy, thereby heralding a major transformation in healthcare. Conversely, it also acknowledges inherent challenges such as issues of interoperability, data privacy concerns, and the need for methodological standardization. With an attempt to shed light on potential avenues for future research, this comprehensive survey accentuates the prospects of deep learning, while underscoring the integral need to address associated hurdles to ensure its effective implementation in healthcare."