Reliable fault detection in high-voltage circuit breakers is essential for ensuring power system availability and reducing maintenance-related downtime. This paper proposes a hybrid machine learning framework for binary fault detection using vibration and acoustic signals acquired during circuit breaker operations. The framework integrates signal processing and feature engineering techniques, including Fast Fourier Transform (FFT), signal decomposition methods (VMD and EMD), and dimensionality reduction techniques (Random Projection, PCA, and Kernel PCA). The extracted features are classified using a Neural-Adaptive Tabu Search (NATS) model, which combines the nonlinear learning capability of artificial neural networks with the optimization capability of Adaptive Tabu Search. Experimental studies were conducted under normal and multiple fault conditions. Comparative results demonstrate that FFT-based feature extraction combined with Random Projection (RP) provides the most effective feature representation, while the proposed NATS classifier achieves binary classification accuracies exceeding 92% across all operating scenarios. Compared with VMD- and EMD-based approaches, the FFT-RP framework offers superior diagnostic performance with lower computational complexity. The results indicate that the proposed hybrid AI framework provides an effective and practical solution for real-time condition monitoring and intelligent fault detection of high-voltage circuit breakers, supporting predictive maintenance strategies in modern power systems.