This research investigates nature-inspired quantization for better performance and efficiency in deep learning models particularly with application of them in medical diagnostics and thereby also proposes a hybrid methodology of using the these algorithms. The study blends quantum-inspired learning vector quantization, firefly algorithm, cuckoo search, artificial bee colony, and bat algorithm to streamline the computational requirements without compromising on the accuracy of these models. The proposed parameter optimization models use a series of nature-inspired algorithms to be able to manipulate these memory and processing power requirements, leading them capable of working in edge devices and mobile platforms. The research targeted some crucial medical diagnostic models to prove the algorithmic generalization over the medical diagnostic scope such as brain tumor detection, pneumonia diagnosis, Alzheimer’s disease classification, and ocular detection with global state-of-the-art results in terms of speed, model compression, and energy metrics by compromising only marginal accuracy loss. By utilizing the most effective portions of each algorithm, a hybrid algorithm can be developed that combines the best performing two algorithms to get the final quantized model and thus allows real-time diagnostic support on low-resource devices at scale leading to a high-performance solution. The provided evidence shows that this method is effective in creating quantized models that are reliable, precise, fast, and efficient, making them suitable for use in diagnostic scope.