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The spatial, severity, and frequency of wildfires are growing all over the world, which enhances the necessity to effectively restore forests after the fire under changing climatic and disturbance regimes. Machine learning (ML) has become a strong analytical tool to aid restoration planning with the integration of high dimensions of ecological, climatic, and remote sensing data. But the current uses are still methodologically disjointed, ecologically unsound, and loosely linked to operational decision making. The review critically combine the application of ML in the restoration of post-fire forests, and assesses methodological rigour, ecological relevance, scalability, interpretability, and practical application. A systematic review protocol was taken as the basis, which uses peer-reviewed articles released during the last 10 years by leading scientific databases. An analytical framework that includes the type of model, source of data, spatial-temporal scale, validation practices, ecological grounding, and decision-support relevance was applied to screen and evaluate the studies. The synthesis shows that tree-based and deep learning models are capable of high predictive accuracy of burn severity mapping and vegetation recovery, but no external validation, quantification of uncertainty, or long-term prognosis is done. Most of the models are more performance-oriented as opposed to ecological plausibility, which makes them less transferable across fire regimes and forest types. Bottlenecks also exist in interpretability and incorporation into the processes of restoration. The conclusion of the review is that ML has the potential to serve as an important contribution to post-fire restoration when integrated within ecologically savvy, explainable, and uncertainty-conscious models. The future of predictive advances must be operational and policy-relevant restoration results, which can only be achieved through hybrid models to bridge the gap between predictive improvements and operational and policy-based ecological models, long-term monitoring, and participatory decision-support systems.