Intravenous (IV) therapy is crucial in modern healthcare, but manual monitoring of IV fluid levels is prone to errors and time-consuming. This project introduces an intelligent IV monitoring system that automates the process, improves accuracy, and alerts healthcare providers when fluid levels are low. At the core is an ESP32 microcontroller (MCU), which integrates components like a 3KG load cell to measure the IV bottle’s weight, with data amplified by an HX711 amplifier and transmitted via SPI protocol. Users can select IV bottle sizes (250 ml, 500 ml, or 1 L), and an LCD screen displays essential information, such as bed number and remaining fluid percentage. The system features an alert mechanism that triggers a buzzer when fluid levels drop below a threshold, ensuring timely medical intervention. Additionally, real-time data is uploaded to the Think Speak cloud via the ESP32's internet capability, allowing remote monitoring and analysis. Integrating of machine learning (ML) into IV monitoring provides real time prediction induce detection and adaptive control. This automation reduces human error, eases the workload on healthcare staff, and enhances decision-making through real-time alerts and cloud-based data visualization. Future improvements may involve adding sensors for comprehensive monitoring and refining system accuracy and the user interface, making it a scalable solution for managing IV therapy in medical settings.