The research presents DenseFed-PSO, a novel approach for Alzheimer's disease prediction, harnessing the power of DenseNet, Particle Swarm Optimization (PSO), and Federated Learning. Alzheimer's disease is a pressing global health concern, and accurate early detection is crucial for effective intervention. In this innovative model, DenseNet serves as the foundation, capitalizing on its proficiency in image-based tasks. PSO is applied at the client level, enabling local parameter optimization tailored to each dataset's unique characteristics. This individualized fine-tuning enhances the model's precision and ensures adaptability across diverse data sources. Federated Learning orchestrates the collaboration between multiple clients, preserving data privacy and decentralizing the learning process. Clients’ devices remain the custodians of their sensitive medical data, mitigating privacy risks associated with centralized systems. This decentralized approach enhances scalability and fault tolerance. The synergy of these components results in a robust and accurate Alzheimer's prediction model. Local PSO optimizations yield refined parameters, aggregated at a central server to enhance the global model iteratively. This process not only ensures continuous model improvement but also minimizes communication overhead. Furthermore, this approach extends beyond Alzheimer's prediction, offering versatility for other medical image analysis tasks. It encourages community collaboration among healthcare institutions, fostering a collective effort to combat Alzheimer's disease. While promising, the model's efficacy relies on data quality, system design, and PSO's optimization capabilities. Rigorous validation and evaluation are essential to gauge its real-world impact. ‘DenseFed-PSO’ signifies a significant step toward early Alzheimer's detection, privacy-preserving AI in healthcare, and collaborative medical research.