Orthodontic treatment, including impacted canines, is a complex process that requires accurate and timely detection. Traditional methods for identifying impacted canines typically rely on manual inspection, which can lead to human errors and time-consuming processes. Canine impaction analysis has numerous applications across various fields such as aesthetics, self-esteem, early intervention, maxillofacial health, and improved quality of life. By leveraging the power of deep learning, this model aims to revolutionize the field of orthodonture and contribute to improved patient outcomes. An approach has been proposed that utilizes image processing methods combined with fast region-based convolutional neural networks (fast R-CNN) to automatically detect impacted canines. This method streamlines the diagnosis process and enhances the accuracy of treatment planning. The method also employs Python for statistical analysis to determine whether treatment for canine impaction is feasible. The proposed method has demonstrated high performance with an accuracy of 98.3%. This collaboration is crucial to ensure the responsible and effective integration of this technology into clinical workflows, ensuring its seamless incorporation with ethical considerations for optimal effectiveness.