The real estate market is rapidly growing and experiencing rapid fluctuations, and there is a need for a system that analyzes the changing trends and predicts house prices in future. As urban areas like Bengaluru, known as silicon valley of India are experiencing faster growth in real estate properties due to surge in the growing land and infrastructure. Limited studies focus on applying analytical data visualization techniques to extract insights from housing price trends. This research paper integrates various data analytical and statistical methods to analyze and benchmark the real estate price market. This research also presents a comparative analysis of machine learning models for house prediction in different parts of Bengaluru. The dataset contains various attributes such as type of area, location, availability, size, society, total_sqft area, bathrooms and balconies. The performance of diverse models including Linear Regression (LR), Support Vector Regressor (SVR), AdaBoost Regression, K-Nearest Neighbours (KNN) Regression, Lasso Regression, Ridge Regression, was assessed and performance metrics were evaluated to predict prices in Bengaluru. The results of the study revealed that Linear Regression achieved a prediction accuracy of 86%. The capacity of this model to develop non-linear relationships within the dataset enhanced the efficiency of the prediction process benefiting the buyers and sellers by managing, controlling and tracking flow of budget. By evaluating the performance of models in different scenarios, this research provides valuable insights enhancing the process of decision making in house price prediction.