Rapid advances in deep learning and artificial intelligence (AI) technologies have revolutionized medical imaging and opened up previously unheard-of possibilities for early disease detection. This study explores two deep learning algorithms, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for use in the interpretation of medical pictures, including X-rays, MRIs, and CT scans. We review the effectiveness of these algorithms in identifying early-stage diseases, such as cancers, cardiovascular conditions, and neurodegenerative disorders, highlighting their potential to enhance diagnostic accuracy and improve patient outcomes. We also go over how these algorithms might be incorporated into clinical workflows, addressing issues with ethical considerations, model interpretability, and data variability. When it comes to illness detection, deep learning models outperform conventional imaging analysis techniques by utilizing massive datasets and cutting-edge training techniques. This project intends to add to the expanding corpus of research on artificial intelligence in healthcare by promoting the use of cutting-edge imaging technology to enable prompt diagnosis and treatment.