Traditional X-ray analysis in dentistry is time-consuming and prone to errors due to complex interpretations. This study explores an automated approach to identifying various dental problems, including fillings, implants, impacted teeth, and cavities, through the application of sophisticated ML algorithms. A highly accurate and interpretable convolutional neural network (CNN) model is being developed for automated disease classification in dental X-rays. This custom CNN rained on a curated dataset encompassing a variety of conditions, achieved an impressive validation accuracy of 94.13%, greatly surpassing the performance of pre-trained models such as DenseNet201 (74.9%), YOLOv8 (80.7%), and YOLOv9 (81. 3%). By applying this methodology this custom CNN can precisely and rapidly identify dental anomalies and significantly enhancing the diagnostic process. This approach not only reduces the diagnostic workload for dental professionals but also ensures timely and personalized patient care. This study highlights the ability of ML in revolutionizing dental diagnostics opening the way for more efficient and accessible dental healthcare services.