Continuous activity monitoring of dairy cattle is essential to acquire a comprehensive knowledge on health and well-being of the animals. In this research, we have reported the development and deployment of “MOOnitor”, a neck-mounted intelligent IoT device for cattle monitoring. The device facilitates classification of salient activities of cattle through appropriately positioned sensors. MOOnitor is an integration of a temperature sensor, a global positioning system (GPS) module, and a 3-axis accelerometer in a lightweight enclosure, which is attached to a halter that allows transmission of data to an IoT server using a microcontroller and a cellular GSM module. After acquiring the necessary sensory information, the most significant features were strategically extracted for enhanced data interpretation. Thereafter, optimally tuned eXtreme Gradient Boosting (XGBoost) and Random Forests classifiers were implemented to classify activities like ‘standing’, ‘lying’, ‘standing and ruminating’, ‘lying and ruminating’, ‘walking’, and ‘walking and grazing’. The performances of the two classifiers towards identification of different cattle activities were compared in terms of accuracy. Furthermore, the importance of using a temperature sensor and a GPS module in addition to an accelerometer in cattle activity recognition could be justified. An overall classification accuracy as high as ~97% was achieved using the XGBoost based classifier. In addition, accuracy, precision, sensitivity and specificity for standing (0.98, 0.97, 0.97, 0.98), lying (0.97, 0.90, 1, 0.96), standing and ruminating (0.99, 1, 0.97, 1), lying and ruminating (0.99, 1, 0.83, 1), walking (1, 1, 1, 1), and walking and grazing (0.99, 1, 0.75, 1) shows the suitability of the proposed method in effective cattle activity monitoring. Since cattle activity states are indicative of various factors such as estrous and several diseases like mastitis, foot-and-mouth disease, etc, the MOOnitor may be used for early detection of these conditions in addition to general health monitoring.