Geothermal energy holds significant potential as a renewable energy source, aligning with sustainable development goals to reduce carbon emissions. This study explores the potential impact of geothermal energy in reducing environmental harm caused by fossil fuels. The African continent, particularly the East African Rift System (EARS), offers vast geothermal potential. Tanzania, for instance, has an estimated theoretical geothermal capacity of over 5000MWe, presenting a reliable and sustainable energy option. In line with this the Tanzania Geothermal Development Company (TGDC) has so far identified more than 52 potential areas where geothermal resources can be found and has taken steps into further exploration. To optimize geothermal energy generation, studies emphasize the importance of understanding system architectures and design principles. AI plays a crucial role in this optimization, offering advanced technologies to analyze energy system patterns and generate meaningful insights. Machine learning algorithms and deep learning methods analyze industrial records and geological data to improve system efficiency and predict future sustainability. Digital twin an AI-based smart technology emerges as a transformative tool, allowing real-world modelling of geothermal plants for optimal operational efficiency. By dynamically changing system variables and observing real-time effects, digital twins enable continuous improvement and integration with AI ecosystems for ongoing optimization. Despite these advancements, challenges remain, including model accuracy and data quality. Ongoing research is needed to refine AI models, address uncertainties, and explore additional applications to optimize geothermal energy production. There is need to further perform computational based simulation modelling in establishing the required optimization parameters.