For early diagnosis, prompt action, and better patient outcomes, accurate medical condition forecasting is crucial. The capacity of deep learning, a branch of AI, to understand complicated patterns from massive volumes of medical data has made it a potent tool in predictive healthcare. Using state-of-the-art deep learning methods including Transformer architectures, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), this research introduces a successful model for medical condition predictions. To improve the accuracy of predictions, the model incorporates a wide variety of data sources, such as EHRs, clinical notes, medical imaging, and time-series vital signs. Cardiovascular events, diabetic complications, cancer progression, and neurological problems are only few of the diseases that the suggested method shows strong performance in forecasting using feature extraction, temporal pattern recognition, and multimodal data fusion. Precision, recall, F1-score, and AUC-ROC are some of the performance measures used to assess the model's efficacy once it has been trained and validated using real-world clinical datasets. When compared to more conventional methods of machine learning, the results show a substantial improvement. The study emphasises that when using deep learning for medical forecasting, interpretability, data quality, and ethical issues are crucial.