This study explores the optimization of intelligent algorithms in real estate marketing with the concept of utilizing machine learning (ML) and artificial intelligence (AI) to optimize marketing. Upon analyzing and investigating the current marketing practices and researching the market potential of algorithmic optimization, the study utilizes use of quantitative techniques such as a survey comprising 200 real estate professionals. In the study, a number of algorithms are considered, including Neural Networks (NN), Support Vector machines (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) on such critical performance indicators as accuracy, precision, recall, and F1-score. The obtained results prove that NN is more effective at its predictive behavior than other algorithms, wherein the accuracy is 90% and a F1-score is 0.88, meaning that it is the best algorithm to predict the lead generation and market trends. In addition to this, smart algorithmic optimized campaigns resulted in a massive lead generation and return on investment (ROI), as some campaigns delivered an 83% increase in leads. Such results demonstrate the value of AI-based algorithms in transforming real estate marketing as it allows selective targeting, better resource use, and real-time decision-making, and in the end increase marketing effectiveness and efficiency.
Keywords: Optimization, Machine learning, Artificial intelligence, Neural Networks, Algorithmic optimization, Data-driven decision-making, Real-time decision-making, Lead generation, Return on investment.
How to cite this article: Sun X. and Zhongxuan Yang Z. Research on Optimization of Intelligent Algorithms for Real Estate Marketing. Int J Drug Deliv Technol. 2026;16(5s): 11-19. DOI: 10.25258/ijddt.16.5s.2