DifEvoDenseFed is introduced as an innovative solution for Alzheimer's disease prediction, leveraging the combined strengths of Differential Evolution Optimization (DifEvo), DenseNet, and Federated Learning (Fed). The urgency of Alzheimer's as a global health concern necessitates accurate early detection for effective intervention. In this advanced model, DenseNet forms the cornerstone, capitalizing on its proficiency in image-based tasks. At the client level, Differential Evolution Optimization (DifEvo) fine-tunes model parameters locally, adapting to the unique characteristics of each dataset. This individualized optimization enhances precision and ensures adaptability across diverse data sources. The collaboration between multiple clients is orchestrated by Federated Learning, preserving data privacy and decentralizing the learning process. Clients retain control of their sensitive medical data, reducing privacy risks associated with centralized systems. This decentralized approach improves scalability and fault tolerance. The synergy of these components culminates in a robust and accurate Alzheimer's prediction model. Local Differential Evolution (DifEvo) optimizations refine parameters, which are aggregated at a central server to iteratively enhance the global model. This process ensures ongoing model improvement while minimizing communication overhead. Moreover, the versatility of this approach extends beyond Alzheimer's prediction, making it suitable for various medical image analysis tasks. It fosters community collaboration among healthcare institutions, promoting a collective effort to combat Alzheimer's disease. It is important to note that the model's effectiveness is contingent upon data quality, system design, and DifEvo's optimization capabilities. Rigorous validation and evaluation are essential to measure its real-world impact. “DifEvoDenseFed” marks a significant stride in early Alzheimer's detection, privacy-preserving AI in healthcare, and collaborative medical research.