The early and rapid detection of infectious diseases is critical for effective disease management and outbreak prevention. Conventional diagnostic techniques, such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA), offer high sensitivity but suffer from long processing times, high costs, and the need for specialized laboratory infrastructure. This study explores the development of a next-generation dielectric-based biosensor that enables real-time, label-free, and highly sensitive detection of infectious agents. The proposed biosensor utilizes high-frequency dielectric transducers, microfluidic integration, and AI-enhanced data processing to detect pathogens based on permittivity and capacitance variations. The methodology involves biosensor fabrication, experimental validation using Escherichia coli, Salmonella enterica, and Influenza A virus, and comparative analysis against conventional diagnostic methods. Expected outcomes include improved detection sensitivity (~0.1 CFU/mL), reduced response times (10–20 minutes), and enhanced field deployability. The integration of machine learning algorithms refines signal interpretation, reducing false positives and improving classification accuracy. Future research will focus on multiplex detection, biosensor miniaturization, and scalable production techniques to expand the applicability of dielectric biosensors in public health surveillance, point-of-care diagnostics, and global epidemic response systems. This study establishes dielectric biosensing as a transformative tool for infectious disease detection, offering a cost-effective, portable, and high-performance alternative to traditional diagnostic platforms.