We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%.]]> ]]> ]]> ]]>