Modern intelligent electrical networks, commonly referred to as smart grids, operate under conditions of increasing uncertainty caused by rapidly fluctuating load demands, high penetration of renewable energy sources, and complex nonlinear interactions among system components. The integration of distributed generation units, such as photovoltaic and wind energy systems, introduces significant variability and intermittency into power supply regimes. Under such conditions, conventional forecasting and control strategies, which are typically based on static models and predefined operating rules, often fail to provide sufficient adaptability, robustness, and efficiency. As a result, power system operators face challenges related to energy losses, voltage instability, and reduced reliability of grid operation. In this context, the development of intelligent forecasting and adaptive control approaches capable of responding to real-time operating conditions has become a critical research direction for sustainable and resilient energy systems. This study develops a machine learning-based forecasting and adaptive control framework to optimize energy flow in intelligent electrical networks under uncertain load and renewable generation conditions. The methodology consists of four main stages: data preparation, forecasting model construction, adaptive control and constrained optimization, and simulation-based validation. Historical and real-time operational data are organized into an input–output dataset including load demand, renewable generation output, voltage and frequency deviations, and operational constraints. A neural network-based time-series forecasting model is employed to predict short-term load dynamics and system operating states, capturing nonlinear temporal dependencies. Based on the predicted states, an adaptive control layer computes real-time control actions using a constrained optimization strategy to minimize energy losses while maintaining system stability and satisfying technological and safety constraints. The framework supports bidirectional energy exchange between the main grid and distributed renewable energy sources and is designed for real-time operation within a smart grid architecture. Simulation studies are conducted using a representative intelligent electrical network model operating under variable load conditions and renewable energy penetration. The proposed framework is evaluated in terms of forecasting accuracy, energy efficiency, and dynamic stability and compared with conventional forecasting and control strategies. The results demonstrate that the machine learning-based forecasting model improves short-term load prediction accuracy by approximately 15–20%. In addition, the adaptive control mechanism enables more efficient energy flow regulation, resulting in a reduction in total energy losses by about 10–12%. The system also exhibits improved dynamic stability, characterized by faster convergence to steady-state operating conditions and reduced sensitivity to load fluctuations. The obtained results confirm that the integration of machine learning-based forecasting with adaptive control mechanisms significantly enhances the performance of intelligent electrical networks under uncertain operating conditions. The proposed framework improves operational stability, efficiency, and resilience and can be effectively integrated into smart grid decision-support systems and digital twin platforms. Overall, this study demonstrates the potential of machine learning-based forecasting and adaptive control for sustainable, reliable, and efficient smart grid operation.