This study aims to evaluate the relative importance of the pillars and indicators within the Energy Transition Index (ETI) and their influence on overall ETI performance. Using a dataset from the World Economic Forum's 2024 ETI report, which covers 120 countries, this research applies Bayesian Belief Networks (BBNs), a probabilistic graphical modeling technique suited for analyzing complex interdependencies among variables. Two models are developed: one connecting the ETI to its eight pillars, and another linking it to 22 selected indicators. Findings reveal that the finance and investment pillar has the strongest positive association with ETI scores, whereas innovation is the area with the highest concentration of low-performing countries. Additionally, strong synergies are observed across digital infrastructure readiness and education quality, highlighting opportunities for multi-dimensional policy interventions. The study provides actionable insights for policymakers, such as prioritizing financial instruments, strengthening regulatory frameworks, and enhancing educational and digital infrastructure to accelerate progress in energy transitions.