This work aims to highlight the distinctions between deep learning models and traditional machine learning methods in colon cancer detection. Based on the LC25000 dataset, we analyze the performance of Support Vector Machine (SVM) and Random Forest (RF) compared to advanced deep learning models such as InceptionV3, VGG19, and EfficientNetV2. Our methodology involves a detailed analysis of each algorithm’s Recall, F1 score, Precision, and accuracy. The results of this research show that deep learning algorithms, and EfficientNetV2 in particular, have outperformed traditional machine learning methods. These results highlight the need to enhance colon cancer detection accuracy by adopting deep learning technology in medical imaging diagnosis.