The machine learning algorithms used for the automated predction of esophageal cancer, a highly destructive disease that frequently exhibits at an advanced stage because it lacks early symptoms, is examined in this work. A huge dataset including clinical, demographic, and diagnostic information is used in the study to relate the classification algorithms of logistic regression and decision trees. With a incredible accuracy of 99.75% compared to 64.87%, the Decision Tree model clearly beaten Logistic Regression. Its capability to recognise complex, non-linear relationships in the data is responsible for Decision Tree's better performance, which makes it a useful tool for early cancer detection. The study identifies the need for validation on a variety of datasets and techniques to reduce overfitting, even as the results illustrate how machine learning may improve diagnostic accuracy. Improved clinical processes and better patient outcomes are made possible by us research's successful development of scalable, interpretable, and effective diagnostic tools.