The most accurate way to measure galaxy redshifts is using spectroscopy, but it takes a lot of computer power and telescope time. Despite their speed and scalability, photometric techniques are less precise. Thanks to large astronomical datasets, machine learning has become a potent technique for increasing cosmology research’s scalability and accuracy. On datasets such as the Sloan Digital Sky Survey, algorithms such as K-Nearest Neighbors, Random Forests, Support Vector Machines, Gradient Boosting, and Neural Networks are assessed using metrics like R-squared, Mean Absolute Error, and Root Mean Square Error. Ensemble approaches provide reliable accuracy, whereas neural networks are excellent at capturing nonlinear correlations. Improvements in feature selection, hyperparameter tuning, and interpretability are essential to improve machine learning applications for photometric redshift estimation and provide deeper insights into cosmic structure and development.