This research introduces an innovative method for intelligent fire detection utilizing autonomous drones equipped with a Vision Transformer (ViT) model. The technology utilizes airborne monitoring and deep learning to detect fire outbreaks in real-time with improved precision and scalability. A dataset of 10,000 labelled aerial images, encompassing fire and non-fire events, was utilized to train and evaluate the model. The proposed ViT-based system achieved an overall accuracy of 96.4 %, markedly improving traditional CNN-based models such ResNet50 and MobileNet, which reached accuracy of 91.2 % and 88.7 %, respectively. The system exhibited a precision of 95.7 %, a recall of 97.1 %, and an F1score of 96.4 %, signifying dependable performance across various environmental conditions. Field testing involving autonomous drone deployment in three simulated forest areas demonstrated a real-time fire detection latency of less than 2.3 seconds. These findings highlight the capability of ViT-integrated drones for quick autonomous fire detection, facilitating expedited response and reduced risk to human life and property. © 2025 Elsevier B.V., All rights reserved.