This study outlines the creation and execution of an IoT system for monitoring and analyzing environmental conditions in hydroponic green-oak cultivation. Optimize plant growth using automated control and real-time data analytics. The system uses sensors to measure temperature, humidity, and pH, transmitting data through WiFi to a cloudhosted MySQL database. A Grafana dashboard handles visualization and user interaction, and KNIME Analytics Platform is used for data analysis and model training. The research took place in a regulated hydroponic setting, focusing on green oak as the target crop. Data on growth metrics such as leaf width, height, root length, and biomass weight were gathered from 30 samples. We applied multiple linear regression and random forest models to evaluate how environmental variables affect crop yield. The Random Forest model showed strong predictive accuracy, achieving an R
2
of 0.86, an MAE of 9.23 g, and an RMSE of 12.37 g. Results indicate that pH significantly impacts growth, whereas high humidity adversely affects yield. This study presents a scalable framework for smart farming utilizing affordable embedded systems. The results lay the groundwork for intelligent, data-driven agricultural control systems, even without a baseline comparison to non-IoT environments.