The greenhouse is a commonly model built to plant muskmelon or cantaloupe, in which the growing environment is efficiently controlled and pests are reduced. However, the model is still limited in terms of irrigation methods, monitoring and controlling crop indicators. In this study, a comprehensive, real-time data monitoring and control system has been designed to enhance agricultural productivity and standardize crop quality. The system incorporates four distinct irrigation control modes are fixed-timer, cyclic programming, manual override, and a critical automated mode which regulated by dynamic soil moisture levels. At the field level, a Delta Programmable Logic Controller (PLC) utilizes the MODBUS RTU protocol to acquire essential environmental and hydrological data. This encompasses soil and ambient temperature/humidity, CO₂ concentration, and critical water parameters (flow, pressure, and level). At the cloud level, the PLC subsequently interfaces with a Navismod gateway, facilitating robust MQTT protocol transmission of all collected telemetry to a cloud-based server. Finally, the user interface leverages an HTTP API for seamless two-way data exchange, providing capabilities for real-time parameter visualization, remote control execution, and critical event alerting. In this paper, a fuzzy-based regulator is proposed to control water pump’s flow to efficiently supply the greenhouse via the collected the growing environment data. The proposed Real-time Fuzzy-based Internet of Things (RFIoT) technology enhance operational efficiency, reduces energy consumption, and crucially sustains optimal soil moisture content, thereby ensuring a superior growing environment. The practical effectiveness of the system was rigorously validated through a two-month field trial conducted in Phu Giao District, Binh Duong Province. Sensor accuracy was demonstrated to be exceptionally high, with error margins ranging narrowly between ±1.92% and ±3.18%, confirming its suitability for precision agricultural monitoring. Based on the real-time collected data, the operator can be actively make decisions about how to control the greenhouse. This robust data stream provides cultivators with the necessary intelligence to accurately evaluate controlled environment conditions and implement data-driven optimization of irrigation planning.