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The urgent need for sustainable development is driving transformative changes in critical sectors such as agriculture and energy. Artificial Intelligence (AI) is playing a pivotal role in this transition by enabling advanced decision-making processes, optimizing resource management, and promoting environmentally sustainable solutions. Our research advances this shift on three cutting-edge fronts: the development of a Digital Twin framework for precision agriculture, which enables real-time crop monitoring and adaptive forecasting; the design of MATNet, a multimodal transformer-based architecture for accurate day-ahead photovoltaic power forecasting; and the application of multi-agent reinforcement learning strategies to optimize energy sharing and management within energy communities, with a focus on operational efficiency and privacy preservation. Collectively, these initiatives demonstrate the transformative potential of AI-driven approaches in advancing sustainable practices across interconnected domains.