The increasing global energy demand and the transition toward sustainable energy systems have highlighted the importance of energy storage technologies by ensuring efficiency, reliability, and decarbonization. This study reviews chemical and thermal energy storage technologies, focusing on how they integrate with renewable energy sources, industrial applications, and emerging challenges. Chemical Energy Storage systems, including hydrogen storage and power-to-fuel strategies, enable long-term energy retention and efficient use, while thermal energy storage technologies facilitate waste heat recovery and grid stability. Key contributions to this work are the exploration of emerging technologies, challenges in large-scale implementation, and the role of artificial intelligence in optimizing Energy Storage Systems through predictive analytics, real-time monitoring, and advanced control strategies. This study also addresses regulatory and economic barriers that hinder widespread adoption, emphasizing the need for policy incentives and interdisciplinary collaboration. The findings suggest that energy storage will be a fundamental pillar of the sustainable energy transition. Future research should focus on improving material stability, enhancing operational efficiency, and integrating intelligent management systems to maximize the benefits of these technologies for a resilient and low-carbon energy infrastructure.

The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review examines recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies with elements of bibliometric analysis to identify trends, methodologies, and research directions. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions.