To address the challenge of balancing model complexity and real-time performance in unmanned aerial vehicle (UAV)-based forest fire smoke detection, this paper proposes a lightweight and efficient multi-scale detection algorithm based on an improved YOLOv8n architecture, named LEM-YOLO (Lightweight Efficient Multi-Scale-You Only Look Once). First, a lightweight multi-scale feature extraction module C2f-IStar (C2f-Inception-style StarBlock) is designed to reduce model complexity while enhancing the representation capability for images of flames and smoke that exhibit drastic scale variations. Second, a multi-scale feature weighted fusion module (EMCFM) is introduced to mitigate the information loss and background interference of densely packed small targets during the feature fusion process. Third, a lightweight shared detail-enhanced convolutional detection head (LSDECD) is constructed using shared detail-enhanced convolutions to reduce computational load and improve the model's ability to capture image details. Finally, the complete intersection over union (CIoU) loss function is replaced by the powerful intersection over union (PIoU) loss function to improve the convergence efficiency in handling non-overlapping bounding boxes. Experimental results indicate that, compared with the baseline model, the improved model achieves increases of 1.9 percentage points and 2.5 percentage points in mean average precision at intersection over union of 0.5 and 0.5 to 0.95, respectively, while reducing model parameters by 31.6% and computational cost by 27.2%, and the processing speed reaches 57.82 frame/s. The improved model achieves an effective balance between lightweight design and detection performance.