Dental implants are a well-accepted form of prosthetic alternative for missing teeth. Owing to their demand, over 220 manufacturers have entered the market and produced a variety of implants with different make and shape. This creates a challenge for dental practitioners to identify the implant brand when the necessity arises. There is no well-established method for implant identification, and this calls for a quick and scientific method. Deep learning, a computer vision technique for image recognition, can be a solution, to identify implants in a radiographic image. The objective of the study was to evaluate the efficacy of deep learning in implant identification using radiographs. A dataset of 740 images consisting of Osstem TSIII SA, Dentium superline, and Adin Touareg implant systems was derived from panoramic and periapical radiographs to train the YOLOv8 model in implant identification. The model identified Adin Touareg with 94% accuracy, Osstem TSIII SA with 90% and Dentium superline with 85% accuracy giving an overall accuracy over 90%. In the current study, we found that the YOLOv8 model could accurately identify implants in radiographs even with smaller training datasets, and more cross-sectional studies across the world with diverse implant systems and a larger sample size are recommended for generalizability and to be able to implement the model in a real-time scenario.