Intelligent Transportation Systems (ITS) require reliable vehicle detection, yet model accuracy is frequently compromised by environmental variables. To reduce indeterminacy in complex urban landscapes, this study proposes an improved framework that combines the YOLOv11 architecture with neutrosophic image preprocessing. The suggested hybrid model (YOLOv11 architecture with neutrosophic images) performed better in challenging conditions, such as poor lighting, rain, fog, dust, nighttime, and sunny conditions, when tested on the Smart City Cars Detection dataset. The results reveal an improved Micro-F1 score of 0.4937 and an improvement in mAP@0.5 from 0.5892 to 0.6206. The "Motorbike" class precision increased from 66.67% to 80.00%, indicating that the model successfully identified small items. The Neutrosophic-YOLO11 framework demonstrated higher recall and improved resilience against noise and glare, albeit at a slight trade-off in micro-precision, offering a scalable method for practical smart city applications.