High-temperature thermochemical energy storage (HT-TCES) materials are essential to enable efficient industrial waste heat recovery and widespread deployment of renewable energy. However, determining the most appropriate material requires addressing several interrelated criteria, including thermal stability, reaction enthalpy, cycling behavior, cost, and environmental impact. To manage this complexity, this study proposes a hybrid framework between Decision Tree (DT) and Analytical Hierarchy Process (AHP) as a multi-criteria decision-making (MCDM) methodology integrated with machine learning for systematic HT-TCES material selection. The framework begins with a DT-based feature selection stage, which automatically determines the relative importance of evaluation criteria and filters out less significant attributes from a large initial set. DTs are chosen for their high interpretability and ability to provide explicit, rule-based explanations, allowing decision makers to understand why certain criteria are prioritized. The refined set of critical criteria is then analyzed through AHP, which structures pairwise comparisons and calculates consistent priority weights to rank candidate materials. Applied to high-temperature industrial waste-heat recovery (above 500 °C), the integrated DT–AHP model identifies the most suitable materials by simplifying the decision process, clarifying the choices, and ensuring a robust selection method. Sensitivity analysis shows that the material rankings remain consistent even when input conditions vary. This interpretable ML+MCDM approach offers a scalable decision-support tool for energy planners and policymakers, facilitating the sustainable deployment of thermochemical storage technologies and supporting global decarbonization objectives.