Introduction: Esophageal cancer (EC)is the sixth most common cancer with a high fatality rate. Early prognosis can improve the survival rate of the patients. The sequence of the progress of the EC is from Esophagitis to Non-Dysplasia Barrett__ampersandsignrsquo;s Esophagus to Dysplasia Barrett__ampersandsignrsquo;s Esophagus to Esophageal Adenocarcinoma (EAC). Computer-Aided Diagnosis (CAD) has become a primary tool of the decade to diagnose various diseases. Objective: The recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enriched the potential of detection, localization, and classification of the medical image pattern. Here, we have compiled multiple research works based on supervised DL architectures. Methods: This review focuses on the application of DL techniques for the detection, segmentation, and classification of the various stages leading to the EAC. The surveyor concentrates on the pre-trained classification detection and segmentation models. Results: The advancements in AI have enhanced the contributions in the medical field applications. The technological progress in AI and DL led to a large number of researches in the medical field. The new algorithms and DL models resulted in many automated systems for the detection segmentation and classification of oesophagal cancer. Conclusion: This review discusses the various challenges, limitations, and future aspects of analysing endoscopic images based on DL methods. Further investigations are to be carried out to improve the performance of CAD systems for successful real-time detection of oesophagal and associated stages. It is essential to formulate more collaborated studies with experts in the field.