Considering growing energy demands, this study presents the design and simulation of a smart system to predict electricity consumption, addressing the growing demand for electricity in the Kingdom of Saudi Arabia, particularly in Bisha, driven by increasing population density and reliance on fossil fuels major contributors to greenhouse gas emissions and global warming. The system was developed and simulated using SolidWorks and Proteus, while MATLAB was employed for data analysis and accuracy measurement. The research achieved the creation of a smart system capable of real-time measurement, prediction, and monitoring of electricity consumption, boasting a high prediction accuracy of 98.96%. This predictive capability is crucial for managing demand fluctuations, enhancing energy strategies, and supporting sustainable development. The system incorporates an alert feature that notifies users of abnormal consumption patterns or risks, fostering consumer awareness and encouraging energy-efficient practices. The study also revealed a strong correlation between population density and per capita electricity consumption in Bisha, providing valuable insights for urban planning and resource allocation. The research underscores the transformative role of smart systems in electricity management through real-time monitoring, predictive forecasting, and user alerts. Key advantages include raising user awareness, empowering energy-saving behaviors, and scalability for broader applications. Practical implications include integrating the system into smart home platforms for automation, optimizing energy pricing strategies, and enhancing sustainability. This study highlights the significant potential of intelligent systems to improve energy efficiency, promote sustainability, and support the transition to smarter energy management in response to rising demands and environmental challenges.