The study proposes an IoT-based smart livestock monitoring system that enables continuous health monitoring and supports data-driven decision-making by farmers to enhance dairy farm management. Low-cost, non-invasive sensors monitor body temperature, heart rate, rumination, and calving. Sensors connect to Arduino and ESP8266 microcontrollers to collect data in real time and send it wirelessly to local or cloud computing platforms. Dashboards provide farmers real-time alerts, visual feedback, and health status updates so they can take action early and care for their animals. To make the system smarter and more scalable, it uses predictive analytics and machine learning methods like SVM for health categorization and LSTM for behavior forecasting. The first week-long tests showed that the system was very accurate, with less than five seconds of alert delay, and cows with stable vitals could produce up to 3% more milk. The report discusses moral issues, such as the use of sensors that do not harm individuals and the ownership of data collected from farmers. Sharing data with third parties and encrypting data transfers keep your information safe. This method makes it possible, sustainable, and scalable to use AI in dairy farming.