The growing destructiveness of wildfires, amplified by climate change, demands transparent predictive tools. AI models have advanced wildfire prediction, but their “black-box” nature hinders operational adoption. This short survey investigates explainable AI (XAI) methods for wildfire management and ecological sustainability, focusing on bridging the experimental to operational gap. We systematize XAI techniques (model-agnostic, model specific, hybrid/emerging) into a unified taxonomy, evaluating their interpretability, computational efficiency, and fidelity. Deployed case studies show that XAI integration yields substantial benefits but also reveals critical implementation barriers such as resource constraints and legacy system integration. Synthesizing fragmented literature and empirical insights, this work provides actionable guidelines for selecting the right XAI methods that balance technical stability with operational practicality. It also highlights research priorities to align the advances of XAI with the needs of firefighters, policymakers, and ecosystem sustainability managers to promote climate resilience and biodiversity conservation.