The sudden urban and industrialization has resulted in the unanticipated increase in energy demand, which consequently brought to focus the necessity of new solutions in energy management in smart cities. This study follows the growth in the role of the Internet of Things (IoT) and wireless sensor networks (WSNs) as the primary elements of real-time sensing, predictive analysis, and efficient energy distribution in intelligent infrastructures. In contrast to the current IoT-WSN systems that are restricted to a single factor such as energy efficiency or security, the proposed model will incorporate the IoT-WSN, edge computing, optimization algorithms, and artificial intelligence into one full package in ensuring that all the aspects of the systems scalability, accuracy, latency, and safety are enhanced. The study will involve reading and synthesizing of the available peer-reviewed literature based on some criterion, which was subsequently accompanied by constructing analytical and predictive models. Such models were tested and justified by comparing them with the baseline models and sensitivity analysis of energy demand scenarios. The findings reveal that the proposed framework not only achieved better energy savings and prediction accuracy by 42% and latency by 47% and security threat by 65% of the baseline of the IoT-WSN framework but also enabled it. Besides the technical benefits, the findings point to the social value of sustainable and resilient energy infrastructures in smart cities as improving a low carbon footprint and city resilience. The research concludes that hybrid IoT-WSN systems improve efficiency in operations and open up a route towards future developments such as blockchain-supported energy trading and city-to-city scaling, presenting a sustainable mechanism for urban growth.