Fire in forests and urban areas has been and is still a serious issue all over the world. Owing to the different colors, intensity, textures, and shapes of the fires, the development of a robust and accurate fire detection system is a challenging task. Fire detection in images through computer vision and image processing techniques such as convolutional neural networks (CNNs) has gained significant attention due to its real-time detection capability. However, fire detection using CNNs in the previous studies has limitations such as evaluation on balanced datasets that may mislead information on a real scenario. This study proposed a new approach using modern technology to detect and control fire. An Unmanned Aerial Vehicle (drone) integrated with two state-of-the-art detection models Yolo-v5 and EfficinetDet is used. The deep learning-enabled drone has the capability of patrolling over the potentially threatened fire areas to detect smoke and fire accurately, based on static images and video clips taken through drone cameras. The two detection models Yolo-v5 and EfficinetDet were individually tested using a huge fire dataset in a real wildfire environment and their performances were comparatively evaluated in terms of precision, recall, and accuracy. The experimental results showed that the new approach based on the strong detection models (Yolo-v5 and EfficientDet) has high fire detection accuracy. Both models have satisfactory detection accuracy but observable false-positive rates, however, in the in-depth comparative analysis, EfficientDet outperforms the Yolo model in terms of detection accuracy and as well as in other evaluation parameters.