The rapid urbanization and consequent surge in vehicular density worldwide necessitate advanced, real-time traffic monitoring solutions. This paper presents a robust framework for multi-class vehicle detec tion leveraging the novel YOLOv12n architecture, specifically tailored for intelligent transportation systems (ITS). We train and rigorously evaluate our model on a curated dataset of 535 annotated images comprising 11,035 vehicle instances across three classes: cars, trucks, and buses. YOLOv12n demonstrates superior performance over state-of the-art lightweight detectors, including YOLOv8-nano, YOLOv9-tiny, YOLOv10-nano, and YOLOv11-nano, achieving 94% precision, 91% recall, and 96% mAP@0.5 while sustaining a real-time inference speed of 132 FPS. The architectural innovations of YOLOv12n, particularly its attention-based feature learning and Residual Efficient Layer Aggre gation Networks (R-ELAN), enable robust detection under challenging conditions such as variable illumination, partial occlusions, and signif icant scale variations. This study establishes YOLOv12n as a com pelling solution for practical traffic surveillance and paves the way for advanced smart city applications.