The Solid-State Drives (SSDs) have become critical components of modern computing infrastructures because of their high throughput, low access latency, and increasing storage density. However, SSD failure can cause data unavailability, service interruption, and operational costs, making reliable failure prediction an important research problem. Machine learning provides a promising approach for identifying complex relationships between operational characteristics and impending device failures, but the opacity of predictive models can limit their practical adoption. This paper proposes an explainable machine learning framework for SSD failure prediction that integrates predictive modeling with Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The framework is designed around data preparation, health-indicator construction, feature selection, predictive modeling, model validation, local explanation, global feature attribution, and explanation consistency analysis. The supplied literature demonstrates growing interest in combining advanced computational intelligence with complex predictive and optimization tasks, while the directly relevant reference emphasizes transparency in SSD failure prediction (Kumar, 2026). The proposed framework extends this direction by treating explainability as an integral component of the prediction pipeline rather than an after-the-fact visualization mechanism. Analytical findings indicate that combining predictive accuracy with local and global explanations can improve interpretability, support failure investigation, and provide a more actionable basis for maintenance decisions. The study also identifies limitations associated with explanation stability, correlated variables, temporal degradation patterns, and the domain relevance of the currently supplied literature. The resulting framework establishes a research-oriented architecture for trustworthy and operationally interpretable SSD failure prediction. 

Solid-State Drive Failure Prediction, Explainable Artificial Intelligence, Machine Learning, LIME

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