<p>Power to X (PtX) technologies are transitioning from theoretical concepts to commercially viable options due to increased renewable generation. A key challenge for widespread adoption is the development of robust techno economic evaluation frameworks that account for uncertainties and sectoral interactions. Many current methods do not consider environmental costs and therefore may lead to inefficient investments. This research proposes an innovative analytical framework to evaluate PtX systems in terms of financial performance, operational efficiency, and environmental sustainability. The new framework uses probabilistic financial modeling, hybrid optimization, predictive analytics, and environmental assessments to allow for a comprehensive evaluation of PtX systems. The proposed framework uses Monte Carlo simulation for uncertainty aware financial modeling; a hybrid mixed integer linear programming and genetic algorithm model for system wide optimization; long short-term memory networks for demand supply forecasting; and a combined life cycle assessment and multi criteria decision analysis module for environmental evaluation. Therefore, the framework evaluates all three aspects of the system simultaneously. Results indicate that application of the framework improves economic performance with net present value increases of 15–20% and payback period reductions of up to 30%. Also, results indicate that environmental outcomes improve with greater reduction in greenhouse gas emissions and better renewable energy utilization efficiency. Therefore, the framework is suitable for strategic planning and policy formulation in complex energy systems due to its interpretability and traceable decision pathways.</p>