The AsianVehicle dataset introduces a comprehensive image collection of four traditional Bangladeshi vehicle types—Auto Rickshaw, Rickshaw, Rickshaw Van, and Leguna—captured to support research in computer vision and cultural informatics. These vehicles, integral to South and Southeast Asian transport systems, represent unique visual and structural characteristics seldom documented in existing global datasets. A total of 4000 RGB images were collected across various urban and semi-urban areas of Mirpur, Dhaka, using smartphone cameras under natural daylight conditions to preserve authentic colors, textures, and environmental diversity. The dataset encompasses variations in viewing angles, backgrounds, and illumination, reflecting real-world scenarios where such vehicles operate. All images are provided in properly processed form, enabling users to apply customized preprocessing, and labeling strategies according to their research needs. Beyond supporting machine learning tasks such as vehicle classification, segmentation, or detection, this dataset contributes to the digital preservation of traditional Asian transport designs that are gradually disappearing due to modernization. Its open accessibility facilitates comparative studies on model generalization, cross-domain adaptation, and low-resource visual recognition. By bridging cultural representation and artificial intelligence research, AsianVehicle offers a valuable foundation for both technical innovation and the preservation of regional identity within data-driven applications.