Efficient energy management in microgrids is critical for ensuring stability and sustainability, especially with the integration of Electric Vehicles (EVs). Fuzzy Logic (FL) has emerged as a powerful method for managing uncertainties in renewable energy generation, load demand, and EV behavior. This study introduces a Fuzzy Logic-Based Energy Management System (FL-EMS) designed to optimize energy flow, balance supply and demand, and minimize operational costs in EV-integrated microgrids. The proposed FL-EMS employs a Fuzzy Logic Controller (FLC) that processes key input variables, including renewable energy generation, load demand, grid electricity prices, and EV State of Charge (SOC). A comprehensive set of fuzzy rules is developed to dynamically manage power flow between distributed energy resources, energy storage systems, EVs, and the main grid. Simulation studies conducted under diverse scenarios highlight the system’s efficiency and adaptability. The results demonstrate that the FL-based FL-EMS significantly reduces grid dependency, improves microgrid reliability, and optimally schedules EV charging and discharging to enhance grid stability. Its ability to process imprecise data and adapt to rapidly changing conditions makes it a practical choice for real-time energy management. The decentralized nature of the FL approach ensures scalability, enabling its application in diverse microgrid configurations, from residential systems to industrial networks. This study highlights Fuzzy Logic as a reliable and scalable solution for energy management, facilitating seamless EV integration and paving the way for resilient, adaptive, and sustainable energy systems.