Artificial intelligence (AI) has become a transformative technology in medical diagnostics, enabling enhanced analysis of complex clinical data and supporting precise, efficient decision-making across diverse disease areas. This study explores the multi-disease application of AI in diagnosing cancer, cardiovascular diseases, neurological disorders, and infectious diseases, focusing on its role in improving diagnostic accuracy, speeding diagnostic processes, and facilitating early disease detection. By employing machine learning, deep learning, and neural network models, this study critically examines the performance of specific models – such as recurrent neural networks and support vector machines – in diverse healthcare contexts. Challenges addressed include data privacy, annotated dataset needs, overfitting risks, and ethical concerns such as AI bias and transparency, all of which are fundamental to ensuring patient safety and health equity. In addition, this study integrates security considerations, such as fault detection in cryptographic architectures, providing insights into the resilience of AI systems in healthcare. Future research directions, including the potential of AI in real-time patient monitoring, personalized medicine, and multispectral imaging, are proposed to expand AI’s utility in diagnostics. A comparative evaluation with traditional clinical diagnostics underscores AI’s validation potential, emphasizing its need for robust regulatory frameworks, particularly concerning global health standards (e.g., TRIPOD-AI and CONSORT-AI) and data privacy regulations such as Health Insurance Portability and Accountability Act and General Data Protection Regulation. Ultimately, AI-driven diagnostic systems show strong promise to revolutionize medical practice and improve patient outcomes, contingent on addressing the technical, ethical, and regulatory challenges involved. This research supports AI’s growing role in healthcare, providing a foundational understanding of both its current contributions and future potential across disease-specific applications.