Planning an effective treatment plan and managing patients with neurological and chronic diseases, such as Parkinson’s, Alzheimer’s, and diabetic retinopathy, requires early and accurate diagnosis. This work introduces a thorough method for multi-disease identification based on deep learning models, such as the well-known VGG-16, ResNet-50, and EfficientNet-B0 architectures that excel in computer vision applications. Integrating the predictions from multiple models using their varied architectures and learning capacities improves the system performance by using ensemble learning approaches. Based on comparative examination and intensive testing, the two best-performing models are determined based on diagnostic accuracy. This proposed work involves implementing a fusion method that integrates the advantages of the chosen models to enhance the overall precision and dependability of the multi-disease detection system.