Forest fires are now increasing in recent years with the impact of climate change. Early detection of the smoke is of prime importance for preventing the fast growth of the fire. New advancements in the YOLO-based models for the object detection of YOLOv7 to YOLOv12 models have significantly enhanced the real-time detection of the objects. However, YOLOv12 exhibits limited sensitivity to remote and small-scale fire and smoke regions, as it does not fully capture high-resolution spatial details. To resolve this problem this paper introduces F2-YOLO signifies Fire and Fume-detection, a lightweight and version of YOLOv12, specifically designed for early detection of forest fire and smoke. In this improved network, F2-YOLO integrates three key parts: a Lightweight Deep Adaptive Module (LDAM) responsible for efficient feature sharpening, a Multi-Level Feature Fusion (MFF) module for enhancing features related to small objects, and a Small-Object Detection Head (SODH) for superior localization accuracy. When tested using the ADSF and Wildfire datasets, F2-YOLO is shown to have a mAP@50 of 98.5% and mAP@50-95 of 73.92%, which is greater than other YOLO models.