Artificial intelligence (AI) may fulfill several demands and enhance our daily and professional activities while simultaneously contributing to the escalating energy demand challenge and providing beneficial effects toward a resolution. The utilization of AI, although augmenting our carbon footprint, simultaneously facilitates the evaluation and enhancement of atmospheric greenhouse gas emissions with the objective of mitigation. Research demonstrates that AI is effectively utilized to alleviate anthropogenic climate change caused by environmental degradation and global warming, contributing to the decrease of carbon emissions. The aim is to encourage the AI sector to create efficient models while prioritizing environmental considerations and highlighting sustainable practices throughout the AI lifecycle. Machine Learning (ML) can forecast future energy use and management based on existing conditions using predictive algorithms. This feature can manage energy consumption, forecast fluctuations in carbon emissions based on conditions and time through predictive analyses, optimize energy utilization and carbon output, enhance efficiency, assess waste in production facilities, suggest alternatives, and effectively implement improvement initiatives. Research addressing similar objectives using Deep Learning (DL) models may be conducted using extensive datasets. Natural Language Processing (NLP) applications enhance user interactions, especially in the energy sector. Furthermore, innovative AI models are now utilized in the field, exhibiting effectiveness in carbon footprint assessment and improvement efforts. The environmental impact of AI is a topic of debate, as it has the potential to have both positive and negative effects. This study rigorously analyzes carbon emissions and the impact of AI on the energy sector. Relevant models have been cited in scholarly writings, supported by examples from recent studies. The use of AI in this field encompasses several disciplines and perspectives.