This research is centered on the development of an advanced deep-learning model that utilizes convolutional neural networks (CNNs) for the purpose of vehicle damage detection from image data. The model is trained on a diverse dataset that encompasses various degrees of damage, enabling it to excel in recognizing distinctive damage characteristics within the images. Once damage is detected, computer vision techniques are employed to assess the severity of the damage, taking into account factors such as its size, location, and type. This information serves as a valuable resource for prioritizing and estimating the costs associated with repairs. Furthermore, machine learning techniques, particularly regression analysis, are harnessed to enhance efficiency by predicting repair expenses. This prediction is accomplished by training the model on pairs of cost-related information and corresponding images. The ultimate objective of this research is to create a tool tailored for utilization by insurance companies and vehicle service centers. This tool will streamline the assessment of damage and the estimation of repair costs, thereby contributing to improvements in overall industry efficiency.