Esophageal cancer remains a significant challenge in medical diagnostics due to its subtle symptoms and the complexities of early detection. This paper introduces a transfer learning (TL) technique to enhance the detection of esophageal cancer using endoscopic images. By leveraging pre-trained deep learning models, including VGG16, VGG19, InceptionV3, DenseNet201, ResNet50, and EfficientNetB4, this research addresses the limitations of traditional diagnostic methods. Data augmentation techniques are employed to tackle dataset imbalances, enhancing model performance. Experimental results demonstrate the superiority of DenseNet201 in classifying esophageal cancer images with remarkable accuracy, highlighting the potential of transfer learning in medical image analysis.