The increase in global energy demand is one of the significant environmental and economic challenges of today’s world, especially in terms of sustainability. Artificial intelligence (AI) is playing an increasingly important role in optimizing energy consumption through advanced predictive algorithms, real-time learning systems and integration with IoT technologies and energy management systems. The aim of this study was to analyze the effectiveness of AI tools in the context of sustainability, taking into account carbon footprint reduction and life cycle analysis (LCA) of products and processes. The paper discusses the key challenges of implementing AI in the energy sector, including technological limitations, economic barriers, and ethical and social issues. Particular attention is given to the issue of integrating life cycle analysis with AI models, highlighting the need for data standardization and the development of algorithms that take into account the long-term environmental impact of energy decisions. Examples of practical implementations of AI in energy optimization in industry, construction, transport and renewable energy systems were also presented. The need for further research into energy-efficient AI algorithms, the development of integrated AI-LCA systems and the adaptation of AI to specific economic sectors was also pointed out. It was emphasized that the effective implementation of AI in energy management requires not only technological advances, but also a holistic approach that takes into account environmental, social and regulatory aspects.