["Business Strategy and the Environment, EarlyView. ", "\nABSTRACT\nThis study proposes an AI‐assisted diagnostic framework to analyze the divergence between observed greenhouse gas emissions trajectories and declared climate commitments for 2030. The analysis is based on the integration of EDGAR greenhouse gas emissions data, national climate targets, environmental performance indicators, and Spanish policy projections under scenarios with existing measures and additional measures, due to the fact that these sources allow emissions trajectories, policy benchmarks, and national scenario information to be examined within the same analytical structure. A divergence indicator is calculated to compare trend‐implied 2030 emissions with harmonized policy targets, while Isolation Forest is used as an unsupervised anomaly‐detection tool to identify countries whose combination of projected emissions, target values, divergence levels, and historical emissions trends appears atypical within the selected panel. The results provide descriptive evidence of uneven consistency between declared commitments and projected emissions trajectories, although these findings should be interpreted as diagnostic signals and not as direct measurements of governance effectiveness. It can be observed that the selected European economies tend to present lower divergence values than several non‐European cases, although current trajectories still suggest an incomplete transition toward climate neutrality. The Spanish case provides a more detailed national reading of this tension, due to the fact that projections under existing measures remain above the 2030 target, whereas the scenario with additional measures would substantially reduce the estimated gap. Therefore, the study contributes to the operationalization of climate‐policy divergence as an auditable and reproducible diagnostic object, rather than as a stand‐alone methodological innovation or as a causal assessment of AI‐based governance. Accordingly, the findings suggest that AI‐assisted tools may support environmental policy auditing when they are used with transparent data procedures, cautious interpretation, institutional accountability, and explicit recognition of uncertainty.\n"]