The growing demand for hybrid energy infrastructures worldwide and Australia necessitates the development of sustainable and efficient solutions that are not only cost-effective but also environmental-friendly. However, the performance, financial, and environmental impact of energy infrastructure is limited to the industry, owing to its complexity. Traditional optimisation methods are not feasible for the Australian industry due to their reliance on time-consuming and resource-intensive detailed simulations. To address this challenge, this study introduces an artificial intelligence (AI)-powered simulation and optimisation framework aimed at achieving Pareto optimum solutions for energy infrastructure investments, with consideration of ESG factors. The framework consists of three stages: data governance and simulation based on AI, optimisation with AI-generated data for Pareto Optimum, and ESG and Investment Implementations. Preliminary results indicate that by integrating AI, the proposed framework may significantly enhance simulation efficiency by up to 10,000 times comparing with detailed finite element modelling. With further multi-objective optimisation considering lifecycle assessment (LCA) and lifecycle cost analysis (LCCA), the Pareto Optimum solutions have the potential to reduce lifecycle costs and emissions by up to 55% and 50%, respectively. The findings also demonstrate the potential for ESG investment integration, with long-term lifecycle GHG emissions reduction, promoting social awareness for renewable energies and affordable housing initiatives due to lower lifecycle energy costs from the adoption of renewable energy.