This paper presents the development and implementation of a machine learning-based web service designed to predict residential rental prices in the Ukrainian real estate market. The study employs a comparative analysis of three regression algorithms - Multiple Linear Regression, Decision Tree, and Random Forest - to identify the most effective approach for rental price prediction based on apartment characteristics, including area, number of rooms, floor, building height, proximity to metro stations, pet allowance, and distance to city center. Using data collected from DOM.RIA during November-December 2024, the research demonstrates that the Linear Regression model outperforms more complex algorithms, achieving a Mean Absolute Percentage Error of 4.96% compared to 7.20% for Decision Tree and 5.51% for Random Forest. Feature importance analysis reveals that apartment area, number of rooms, and district location are the most statistically significant predictors of rental prices. The implemented web service architecture provides users with rental price forecasts and confidence intervals, enabling tenants and landlords to make informed decisions in an information-asymmetric market. This research increases transparency and efficiency in real estate transactions by applying accessible machine-learning techniques.