This study investigates the application of Bayesian Belief Networks (BBNs) to optimize thermoforming, an energy-intensive manufacturing process that rapidly shapes thermoplastic composite sheets into precise three-dimensional forms. Thermoforming involves complex interactions among process parameters, presenting ongoing challenges in achieving optimal energy efficiency, temperature stability, and consistent product quality. Further, the process can become highly non-linear with multiple sources of noise, due to the nature of the factory and logistical factors, adding further challenges to effectively predicting the process outcomes. To address this challenge, in this case study a Bayesian Belief Network was developed to capture the inherent uncertainties and intricate relationships among critical process variables. This probabilistic model integrated experimental observations, computational modeling results, and expert-derived insights, enabling a robust and adaptive decision-making support for the process designers. Each of the select key process parameters such as convective Heat Transfer Coefficient (HTC), and heating power, were prioritized based on their influence on the process performance indicators such as the end product quality, energy usage, temperature distribution error, settling time, and stability. Results demonstrated that the BBN framework provides an effective interactive decision support tool capable of continuous model refinements through updating of probabilities as new data became available, while achieving enhanced energy efficiency and process control, thereby also reducing the operational cost and material wastage.