Wildfires pose a growing threat to ecosystems, human safety, and critical infrastructure, driven by accelerating climate change and expanding human activity. Traditional detection systems, such as satellite imaging and manual surveillance often lack the temporal or spatial resolution needed for quick response. Motivated from this, in this paper, we propose, SOAP: S Ave and PrOtect Real-Time Wildfire using Multi-Model Deep Learning Techniques, for early wildfire detection based on object detection and classification during image analysis. Three models are compared using various evaluation metrics as follows (1) YOLOv8 for object detection, (2) YOLOv8-Cls for lightweight binary image classification, and (3) EfficientNetV2-S for high-accuracy deep feature classification. All models were trained and evaluated on a curated wildfire image dataset using standardized pre-processing and augmentation strategies. Performance was assessed using accuracy, precision, recall, F1-score, inference time, and Area Under the ROC Curve (AUC). The EfficientNetV2-S model has achieved the highest accuracy (98.03%) and AUC, while YOLOv8-Cls provided the fastest inference ( 34 ms) suitable for real-time edge deployment. The proposed approach demonstrates the feasibility of deploying AI-driven wildfire monitoring pipelines for fast and more reliable risk mitigation.