A steady and reliable energy supply is essential to modern society since any disruption in the power supply can result in major operational, security, and financial issues. To improve fault detection in electrical grids and mitigate the impact of outages, this research suggests a hybrid machine-learning technique. The model incorporates the Random Forest (RF) and Support Vector Machine (SVM) algorithms, each with complementary benefits to increase the detection system’s accuracy and robustness. SVM excels in high-dimensional classification, effectively distinguishing grid stability states, whereas Random Forest provides ensemble-based robustness, reducing overfitting and enhancing prediction accuracy. When applied in MATLAB Simulink for real-time data simulation, the hybrid model achieved a high accuracy of 96% and showed balanced precision, recall, and F1 scores across stable and unstable classifications. This integrated approach is scalable and economically feasible for various grid scenarios due to its ability to reduce false positives, lower maintenance costs, and enable proactive anomaly discovery. The model has the potential to be a dependable solution for ensuring a consistent and uninterrupted power supply, which is essential for both security and societal advancement, as seen by its remarkable ability to detect flaws.