Smart electric grids produce massive volumes of time-series data that must be monitored continuously to ensure operational reliability. This paper proposes an adaptive anomaly detection framework using Gaussian Mixture Models (GMM) to identify abnormal power consumption patterns in individual household grids. By leveraging temporal features and statistical modeling, our approach detects low-probability consumption events that could signify faults, inefficiencies, or external intrusions. Experimental evaluation on the AEP dataset demonstrates that the proposed GMM framework achieves strong anomaly detection performance, with a precision of 0.89, recall of 0.84, and ROC-AUC of 0.91. Comparative results with threshold-based detection, k-means clustering, isolation forest, LSTM autoencoders, and fuzzy c-means highlight the superiority of GMM in capturing multimodal usage patterns. These findings suggest that the proposed adaptive GMM approach offers a reliable and interpretable tool for real-time anomaly detection in smart household grids.