S.Swathi, Ragiri Manisha, R.Srilekha 02 7(1) The real estate sector is one of the most dynamic and datarich investment domains yet it traditionally relies on manual assessments and intuitionbased decisionmaking With the advent of Artificial Intelligence AI Machine Learning ML and Deep Learning DL investment management in real estate is undergoing a transformative shift This study explores how intelligent technologies can be leveraged to enhance the accuracy efficiency and profitability of real estate investment strategiesThe research applies predictive modeling to historical property data economic indicators market trends and customer behavior to forecast price appreciation rental yields and investment risks ML algorithms such as Random Forest Gradient Boosting and Support Vector Machines are used to analyze factors influencing property values across residential commercial and industrial segments Additionally clustering techniques are applied to segment locations and property types based on investment potential Sentiment analysis using Natural Language Processing NLP helps assess market sentiment by mining news articles social media and buyer reviewsFurther Deep Learning models such as LSTMs are employed to perform timeseries analysis of real estate prices enabling longterm trend forecasting and early detection of market shifts Imagebased DL models also support property condition assessment using aerial and onsite photographs The findings suggest that integrating AI ML and DL into investment management not only improves forecasting precision but also automates routine analysis reduces decisionmaking bias and enhances portfolio performanceThis study concludes that intelligent investment systems can empower real estate investorswhether individuals developers or institutionsto make datadriven timely and optimized decisions ultimately maximizing returns while managing risks in a volatile market environment Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026