This dissertation aimed to enhance dairy health management by integrating sensor technologies and machine learning (ML) algorithms. The research utilized data from automated health monitoring (AHM) systems and other precision livestock farming (PLF) technologies to develop effective health management strategies and predictive models for early lactation disease detection in dairy cows. Chapter II describes a randomized controlled trial comparing high-intensity clinical monitoring (HIC-M) and automated monitoring (AUT-M) programs. Holstein cows (n = 1,249) were monitored for health disorders (HD) and herd performance. Results showed that the AUT-M program, despite a reduced HD diagnosis risk, incorporating visual observation (VO) matched the herd performance of the HIC-M program. No significant differences were observed in milk production, culling dynamics, or reproductive outcomes. Chapter III explored the application of AutoML Lazy Predict and neural networks (NN) to predict dairy cow health status using sensor and non-sensor data. A total of 30 algorithms were evaluated, with the top 8 selected for further refinement. XGBoost (XGB) and Adaboost (Ada) consistently outperformed other algorithms including NN models, with XGB achieving the highest scores due to its ability to handle missing and non-standardized data. Hyperparameter tuning was critical for improving model performance. Chapter IV focused on further developing and validating the XGB model using data from multiple precision livestock technologies. The workflow involved feature selection, model training, optimization through grid search, and resampling techniques. The XGB model demonstrated high performance across most metrics except precision. The best model was tested on a commercial dairy farm for real-time application, but results were not available at the time of submission of this manuscript. In conclusion, integrating AHM systems and other PLF technologies with ML techniques offers a feasible approach to improving health monitoring and management in dairy herds. The research highlights the potential of sensor-based technologies to enhance early disease detection without compromising overall herd performance, contributing to the sustainability and profitability of the dairy industry.