Real estate professionals benefit from precise pricing predictions. This study compared machine and deep learning house price projections. We evaluate linear regression, Random Forest Regression, and the XGBoost regressor. Our study included location, size, number of rooms, amenities, and sales history. Preprocessing includes filling missing values, rescaling features, and encoding categorical variables. Splitting the dataset into training and testing sets allows model evaluation. We employed linear and lasso regression to predict house price changes. MSE and R-squared scores measured model accuracy and readability. We then used a gradient boost regressor to improve decision tree predictions. We optimized hyperparameters and compared our models to regression methods to fine-tune them. Neural networks recorded complex data correlations and patterns. Finding the best structure required several layers and activation functions. Deep learning models were compared to regression methods. Deep learning outperformed regression and ensemble methods in predicting housing values.