Smart farming is seen to be the future of agriculture as it produces higher quality of crops by making farms more intelligent in sensing its controlling parameters. Analyzing massive amount of data can be done by accessing and connecting various devices with the help of Internet of Things (IoT). However, it is not enough to have an Internet support and self-updating readings from the sensors but also to have a self-sustainable agricultural production with the use of data analytics for the data to become useful. In this work, we designed and implemented a smart hydroponics system that automates the growing process of the crops using Bayesian Network model. Sensors and actuators are installed to monitor and control the parameters of the farm such as light intensity, pH, electrical conductivity, water temperature, and relative humidity. The sensor values gathered are used in the building the Bayesian Network, which classifies and predicts the optimum value in each actuator to autonomously control the hydroponics farm. Results show that the fluctuations in terms of the sensor values were minimized in the automatic control using BN as compared to the manual control. The prediction model obtained 84.53% accuracy after model validation and the yielded crops on the automatic control was 66.67% higher than the manual control.