This study introduces a novel hierarchical domain-adaptive learning framework designed to enhance wildfire detection capabilities, addressing the limitations inherent in traditional convolutional neural networks across varied forest environments. The framework innovatively employs a dual-dataset approach, integrating both non-forest and forest-specific datasets to train a model adept at handling diverse wildfire scenarios. The methodology leverages a novel framework that combines shared layers for broad feature extraction with specialized layers for forest-specific details, demonstrating versatility across base models. Initially demonstrated with EfficientNetB0, this adaptable approach could be applicable with various advanced architectures, enhancing wildfire detection. The research’s comparative analysis, benchmarking against conventional methodologies, showcases the proposed approach’s enhanced performance. It particularly excels in accuracy, precision, F1-score, specificity, MCC, and AUC-ROC. This research significantly reduces false positives in wildfire detection through a novel blend of multi-task learning, dual-dataset training, and hierarchical domain adaptation. Our approach advances deep learning in data-limited, complex environments, offering a critical tool for ecological conservation and community protection against wildfires.