Unmanned aerial vehicles (UAVs) provide new possibilities for civilian remote sensing, including automated vehicle detection. This paper proposes a novel approach using Scalar Invariant Feature Transform (SIFT) for feature extraction. This process distinguishes vehicle-related key points from others through machine learning approaches, including SVM, CNN, YOLO, & SSD. Our real-world UAV experiments showcase effective vehicle detection. Vision-based vehicle identification faces challenges due to changing road conditions. In this paper present a cost-effective, real-time, accurate detection method. SVM classifiers with swift Haar-like features detect, while virtual detection lines mitigate false positives. SIFT-based classification improves accuracy and minimizes missed detections. Multi-class classification utilizes YOLO, SSD, CNN, and SVM. Our approach promises robust UAV-based vehicle detection and contributes to intelligent transportation systems.