Recent advancements in artificial intelligence (AI) have substantially impacted healthcare, particularly in disease diagnosis and prediction. This paper introduces an AI-driven decision support system (DSS) designed for multi-disease diagnosis. The DSS leverages custom deep learning architectures: convolutional neural networks, vision transformers, and hybrid models to perform precise and efficient medical image classification and predictive analytics. Image preprocessing is enhanced through masked and variational autoencoders. To enhance model performance and adaptability, the system employs neural architecture search methods guided by stochastic principles. The proposed DSS addresses significant challenges in deploying AI in healthcare, including cross-domain generalization and secure integration within medical settings. Federated learning techniques are incorporated to enable decentralized model training across multiple medical institutions. This integrated DSS showcases the transformative potential of AI in healthcare.