This paper presents an advanced hydroponics monitoring system that interfaces Arduino with a suite of sensors to gather crucial data for optimizing hydroponic crop cultivation. The system incorporates a turbidity sensor for solid particle measurement, a temperature sensor for ambient temperature monitoring, an ultrasonic sensor to gauge water level, and a TDS (Total Dissolved Solids) sensor for assessing dissolved particles in the nutrient solution. The pH sensor in the system will able to detect the pH value of water. The collected sensor data is displayed on an LCD screen for immediate observation. Simultaneously, the system utilizes a NodeMCU (ESP8266) to upload the sensor data to a web server, providing remote access for growers to monitor their hydroponic setup from anywhere. Furthermore, this system employs a machine learning algorithm that processes the sensor data which is sent through serial communication. The algorithm analyses the combined inputs from turbidity, temperature, water level, and TDS sensors to predict whether the current conditions are conducive to optimal plant growth (”good”) or if adjustments are needed (”bad”). The integration of machine learning en hances the system’s adaptability and responsiveness to dynamic environmental changes, offering growers valuable insights for proactive decision-making in hydroponic cultivation. This work serves as a bridge between traditional hydroponics and cutting- edge technologies, promoting sustainable and efficient agricultural practices.