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The photometric redshifts estimation is currently the most powerful and efficient way to estimate the distances to the extragalactic sources. The exponential data avalanche continues and this will require low cost, fast and efficient data-driven methods to analyse and make predictions from the data. In this study, we present the supervised machine learning algorithms that were used to attain the photometric redshifts of the galaxies and quasars found in Sloan Digital Sky Survey data release 16 (SDSS DR16). We adopt the K-Nearest Neighbour (KNN) and Random Forest (RF) regressors to estimate the photometric redshifts of 285685 galaxies and 124688 quasars by considering their photometric measurements.