Accurately matching the shade of dental composite restorations to natural teeth is a crucial aspect of dental treatment, as it can significantly impact patient satisfaction and treatment outcomes. However, the subjective nature of manual shade selection often leads to shade mismatch, with reports suggesting that approximately 35% of composite restorations fail on the first visit. To address this problem, this study proposes a novel approach that utilizes image processing and deep learning techniques for objective and consistent dental shade matching. The developed system employs Convolutional Neural Network (CNN)-based MediaPipe Facial Landmark Detection and Support Vector Machines (SVMs) for objective and consistent dental shade matching. The system achieved an estimated accuracy of 90% compared to traditional manual shade matching. The system has scored an overall average of 4.58 on an initial survey using the ISO 25010 guidelines and 4.64 on a subsequent resurvey, indicating its high agreeability and satisfiability in achieving objective and consistent dental shade matching. The implementation and validation of this application offer a significant technological advancement in dental shade matching, reducing the likelihood of shade mismatch. The findings have significant implications for clinical practice, empowering dental professionals with a reliable tool to improve patient care and satisfaction. This study emphasizes the importance of incorporating advanced technology into clinical practice, ultimately improving patient outcomes.