Timely and precise identification of illnesses is crucial for enhancing patient care and minimizing healthcare expenses. This research introduces a comprehensive strategy to forecast various diseases, including diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia, by combining machine learning and deep learning techniques. The system employs a Random Forest classifier to detect diseases from structured clinical information and a Convolutional Neural Network (CNN) to diagnose malaria and pneumonia using medical imagery. Patient data was collected as input parameters, and the model determined whether an individual was healthy or needed medical attention. Advanced preprocessing methods, such as feature scaling and data augmentation, were utilized to enhance model performance. The system’s effectiveness was assessed using key metrics like accuracy, precision, recall, and F1 score. The findings indicate that the system can deliver dependable predictions across multiple diseases, making it a valuable asset for early diagnosis and preventive healthcare. This investigation underscores the potential of machine learning and deep learning in healthcare by integrating diverse data sources for disease prediction.