The study investigated the design, development, and evaluation of an IoT-based Intelligent Energy Management System (IEMS) aimed at optimizing energy utilization in rural and semi-urban settings in Uganda. The objectives were to design a functional and cost-effective hardware system for real-time energy monitoring, develop robust decision-making algorithms to optimize energy source selection among solar, grid, and battery, and quantitatively evaluate the system’s impact on critical energy performance metrics. The hardware prototype demonstrated high accuracy in monitoring key parameters, including voltage, current, power consumption, solar generation, and battery state-of-charge, with measurement accuracy consistently above 95%. The decision-making algorithms significantly enhanced operational efficiency by dynamically selecting the most cost-effective and reliable energy sources. The system reduced daily energy costs by 44–49%, decreased grid dependency by over 50%, and increased renewable energy utilization to 67% while maintaining full load coverage and ensuring stable voltage supply. Overall system reliability improved to 99%, and voltage fluctuations were reduced by 75%, indicating robust performance under realworld operating conditions. These results confirmed that the integration of high-accuracy IoT monitoring with intelligent optimization algorithms could deliver substantial economic and technical benefits. It was concluded that the IEMS was effective, scalable, and contextually appropriate for Uganda, providing a sustainable solution for energy management that maximized renewable energy use, reduced operational costs, and improved load stability and system reliability. The study recommended scaling up deployment across rural and semi-urban areas, building local technical capacity through training, engaging communities to promote acceptance and ownership, integrating the system with supportive policy frameworks, and continuously optimizing algorithms and hardware based on performance data. Further research was advised to assess long-term impacts, scalability, and economic viability in diverse contexts.