Modern-day IT infrastructures are producing huge amounts of varied telemetry and log data, thus making real-time fault detection and predictive maintenance even more difficult. The traditional rule-based and statistical models do not perform well when it comes to high-dimensional, time-dependent, and imbalanced datasets. So, a hybrid deep learning and ensemble-based predictive framework is suggested, which works by merging Convolutional Neural Network (CNN) with Extreme Gradient Boosting (XGBoost) for the purpose of combining deep temporal feature extraction with ensemble decision optimization. The Microsoft Azure Predictive Maintenance dataset is employed for the purpose of model development and validation. Data preprocessing includes normalization, categorical encoding, and SMOTE-based balancing for the purpose of data integrity and learning stability. The experimental assessment demonstrates that the hybrid CNN+XGBoost model is more effective than the comparison models such as Random Forest, Decision Tree, and Logistic Regression, with an accuracy (ACC) of 98.75%, precision (PRE) of 0.987, recall (REC) of 0.993, F1-score (F1) of 0.990, and an AUC of 0.994. These results highlight the strength, scalability, and excellent predictability of the model. Through the combination of deep learning and an ensemble approach, the framework becomes a robust, adaptive, and smart model when it comes to applying proactive maintenance and operational optimization within the IT industry.

Deep Learning, Ensemble Learning, Fault Detection, IT Operations

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