The decarbonization of energy systems requires efficient and reliable high-temperature thermochemical energy storage (HT-TCES) materials to stabilize renewable electricity supply and meet industrial heat needs. Finding the best HT-TCES materials is a complex multi-criteria decision-making (MCDM) problem, as it involves balancing thermodynamic performance, thermal stability, cost, and environmental sustainability among many potential options. This study presents a machine learning-integrated multi-criteria decision-making framework for the methodical selection of HT-TCES materials. Eighteen material alternatives are rigorously evaluated based on eight crucial criteria, including reaction enthalpy, energy density, operating temperature range, cycle stability, heat transfer characteristics, raw material cost, environmental impact, and scalability. The proposed method uses Multiple-Criteria Ranking by Alternative Trace (MCRAT) as the main ranking tool, supported by three weighting strategies—Analytical Hierarchy Process (AHP) for expert-driven prioritization, CRITIC (Criteria Importance Through Intercriteria Correlation) for objective variability assessment, and MEREC (Method based on the Removal Effects of Criteria) to ensure robustness. A random forest machine learning algorithm verifies the MCDM rankings, identifies non-linear relationships among criteria, and conducts sensitivity analysis to identify the most impactful parameters. The integrated ML-MCDM approach provides consistent, data-based material rankings and highlights key properties that determine HT-TCES suitability. This hybrid framework offers a reproducible and scalable decision-support tool to accelerate the deployment of advanced thermochemical storage systems, fostering improved grid flexibility, industrial decarbonization, and wider adoption of renewable energy.