Early detection of cancer significantly improves survival rates and treatment outcomes. This study aims to enhance prediction accuracy through a combination of strategies namely improving data quality, applying cancer-specific feature engineering techniques, comparing the performance of multiple models, and focusing on non-invasive, cost-effective approaches to early cancer detection. Machine learning (ML) approaches are effectively explored in predicting breast, lung, and liver cancer. The Gradient Boosting approach proved to be the best among 5 other ML techniques. The breast cancer study using histopathological images delivered high accuracy. For lung and liver cancers, high accuracy was achieved despite using only health survey data and demographic information. Even with limited data, the study demonstrates strong predictive performance and offers hope for future cancer detection methods that are fast, affordable, non-invasive, and highly reliable.