Solid-state drives (SSDs) have become fundamental components of contemporary computing infrastructures, yet their failure behavior remains difficult to predict because degradation can emerge from interacting software, workload, device, and operational factors. Conventional predictive systems may achieve useful classification performance while providing limited insight into why a drive is considered at risk. This paper proposes an interpretable artificial intelligence architecture for early SSD-failure prediction that integrates data preparation, feature engineering, machine-learning prediction, and explanation layers based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The architectural rationale is grounded in the provided literature on intelligent software systems, automated learning, system failures, information reuse, and interpretable SSD prediction. The proposed design treats interpretability not as a post-processing feature but as a functional component of the prediction pipeline. A methodological framework is developed around telemetry acquisition, temporal feature construction, risk estimation, explanation generation, and decision support. Analytical findings indicate that the architecture can improve transparency by identifying influential health indicators and distinguishing global failure drivers from instance-specific causes. The study further argues that interpretability can support maintenance prioritization, model validation, and human confidence, although explanations remain dependent on model quality, feature representation, and data distribution. The resulting framework provides a structured foundation for developing reliable and auditable SSD failure prediction systems.