The Traffic congestion is one of the most intricate and challenging problems in all major cities and urban area of Bangladesh. Inadequate road infrastructure is one of the major causes involved with this agonizing issue. The only existing solution to this issue is manual reporting to authority. This study proposes an app-based road state classification, damage detection, and reporting system to assist both the drivers and authority to identify the damaged roads through a proposed web platform. This paper has made various contributions to address the road type classification of Bangladesh. The proposed research work includes the first of its kind road surface classification dataset, prepared in Bangladesh that could be used for applying machine learning techniques. The dataset has been classified in five classes based on the surface condition. The research team then studied some of the state-of-the-art Residual network based machine learning models and later proposed a customized architecture with a smaller number of layers compared to the state-of-the-art Inception-v3 and Inception-ResNet-V2 architectures for classification purpose. The study has explored three different state-of-the-art machine learning models i.e. Inception-v3, Inception-ResNet-v2, Xception for classification and analyzed their results.