Background/Objectives: To overcome the disadvantages of the methods in the literature, such as the reliance on manual measurements requiring a lot of time and effort and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs using artificial intelligence (AI) algorithms. Methods: Two Dimensional Deep Convolutional Neural Network (2D-DCNN) and One Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) are modified and combined and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE and R2 value calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888 and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed therein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.