<p>Advancements in artificial intelligence (AI) and machine learning (ML) are rapidly transforming conventional practices across various engineering fields. Simultaneously, smart grids, particularly distributed power generation systems (DPGSs), have become essential for addressing the growing energy demands of the modern world. The convergence of these two emerging technologies—AI/ML advancements and the proliferation of smart grids—presents a compelling opportunity for optimization. One of the primary challenges in smart grids is the timely identification and mitigation of faults. Failure to address these issues promptly can lead to cascading failures and adverse phenomena such as islanding and blinding effects, which frequently disrupt grid operations. The k-Nearest Neighbors (K-NN) algorithm offers a robust and efficient machine-learning-based approach for fault detection and classification in smart grids. This paper presents an extensive study on the application of various K-NN methods, both with and without grid search, for fault classification in DPGSs. The fault data used for this study is generated through MATLAB and analyzed using Python to evaluate the effectiveness of different K-NN approaches. Given the growing complexity of DPGSs, a thorough analysis of these algorithms is crucial. Experimental results reveal that, after hyperparameter tuning, the Mutual KNN (M-KNN) method outperforms other K-NN variants, making it the most suitable approach for fault classification in smart grids with distributed generation.</p>