Numerous industries make extensive use of machine learning approaches, and the health care sector in particular has profited substantially from machine learning prediction approaches. Disease prediction is a challenging undertaking, thus in order to minimize the dangers involved and notify the patient in advance, the process must be automated. Physicians need accurate forecasts of the course of their patients' illnesses. Rapid developments in Deep Learning (DL) and Machine Learning (ML) have revolutionized the healthcare sector by making medical applications more automated, precise, and efficient. This study investigates how medical imaging, disease prediction, patient monitoring, and customized treatment regimens are all improved by ML and DL models. Healthcare practitioners may decrease medical errors, optimize treatment plans, and increase diagnosis accuracy by utilizing AI-driven models and large-scale information. Convolutional neural networks (CNNs) for medical image processing, recurrent neural networks (RNNs) for patient monitoring, and predictive analytics for early disease identification are some examples of the ML and DL techniques that are integrated in this study. The suggested method improves decision-making skills and automates repetitive operations to increase healthcare efficiency.