This study delves into the techniques and results of detecting exoplanets using transit photometry, leveraging data obtained from the Kepler Space Telescope. The research aims to shed light on both manual and automated approaches to exoplanet detection, with a specific emphasis on employing the folding technique to identify recurring patterns in light curves. Automated detection utilizes the K-nearest neighbors algorithm (KNN), demonstrating a remarkable accuracy of 93%, surpassing human capabilities. This discovery highlights the KNN algorithm's potential as a robust tool in space exploration, offering improved efficacy in identifying potential extraterrestrial life.