The rental of real estate prices is dynamic, and best suited with the application of machine learning algorithms for price prediction. This paper introduces a new model, the Voting Regressor, applied to the task of predicting rental values based on current market conditions. The Voting Regressor increases the accuracy and robustness of predictions by giving its best from several algorithms: Ridge Regression, Decision Trees, SVM Regressor, and Random Forest. The proposed model was tested and validated with a housing dataset containing 4746 records with 12 features detailed both in terms of physical characteristics and geographical information. The dataset was sourced from the premier real estate portal, www.magicbricks.com , through Kaggle. The pipeline followed all steps includes data cleaning, outlier removal, feature engineering, scaling, and hyperparameter tuning. Its performance is checked with some established metrics, such as Mean Absolute Error (MAE) and R-squared (R2), on the way and cross-validation was also applied to check the generality of the model.