Advanced detection systems are required to improve the efficacy of response efforts in the face of the growing threat posed by wildfires to ecosystems and communities. Wildfires' dynamic nature frequently renders conventional detection methods inadequate. This paper introduces FireDetXplainer, a novel framework that is intended to enhance wildfire detection by incorporating transparent and explainable AI techniques. FireDetXplainer guarantees interpretability and clarity in decision-making processes by employing state-of-the-art machine learning models. Our strategy is designed to improve the accuracy of detection and foster stakeholder trust by offering actionable insights into AI predictions. The model's overall accuracy is significantly enhanced by the integration of convolutional blocks and advanced image pre-processing techniques. FireDetXplainer implements Explainable AI (XAI) tools to guarantee comprehensive result interpretation by utilizing a variety of datasets from Kaggle and Mendeley. The FireDetXplainer outperforms current top models and achieves remarkable accuracy, as evidenced by the extensive experimental results. This renders it a highly efficient method for image classification in wildfire management.