Early illness diagnosis is critical for improving healthcare outcomes, and deep learning algorithms have shown promise in raising the accuracy of medical imaging. This research tackles the difficulty of accurate early diagnosis by the deployment of sophisticated deep learning models, notably ResNet-50 and Convolutional Neural Networks (CNNs). The study follows a systematic process starting with issue identification and literature assessment, continuing to the creation and deployment of these models using medical imaging datasets. The ResNet-50 model, recognized for its deep residual learning architecture, obtained an astounding accuracy of 92.5% in diagnosing early-stage disorders, proving its ability in discriminating between distinct ailments. This research also uses CNNs to further boost detection performance. The findings underline the potential of deep learning to transform early illness identification, giving considerable gains over previous techniques. The results add to the field by offering a rigorous framework for using deep learning in medical imaging, with implications for more accurate and quicker diagnosis in clinical practice.