The growing deployment of grid-connected inverters in renewable energy systems has increased the need for reliable, adaptive, and intelligent fault diagnosis and performance monitoring methods. Conventional rule-based and threshold-driven approaches are often inadequate under non-linear operating conditions and dynamic grid environments. This study proposes a machine learning–based data visualization and fault classification framework for inverter condition monitoring that integrates dimensionality reduction, visual analytics, and supervised learning techniques to enhance diagnostic accuracy and interpretability. An inverter operational dataset is preprocessed using missing value handling, feature scaling, and label encoding to ensure data consistency and model reliability. High-dimensional inverter features are transformed using Principal Component Analysis and t-distributed Stochastic Neighbor Embedding to visualize data distributions, detect hidden patterns, identify operational clusters, and reveal anomalous behavior. These visual insights support an improved understanding of inverter health states and operating trends. Three supervised learning algorithms—K-Nearest Neighbors, Support Vector Machine, and Random Forest—are trained to classify inverter conditions and evaluated using accuracy, confusion matrices, and standard classification performance metrics. The results demonstrate that ensemble-based learning models provide superior robustness and generalization capability compared to instance-based and margin-based classifiers. The proposed framework enables early fault detection, improves interpretability through visual exploration, and supports predictive maintenance decision-making. The findings indicate that the integration of machine learning and data visualization offers a scalable and reliable solution for real-time inverter monitoring and can be effectively applied in smart grid and renewable energy management systems to enhance operational reliability and efficiency.