Based on projected climatic trends, the U.S. dairy industry faces escalating heat stress challenges, with milk production losses projected to reach 1.4 kg/day per cow by the 2050s and 1.9 kg/day per cow by the 2080s, resulting in economic losses of $1.7 billion and $2.2 billion, respectively. While extensive cooling mechanisms are in place, they cannot fully mitigate heat stress effects. Thermotolerance, influenced by physiological and behavioral traits, varies significantly among individual cows and is crucial for sustaining dairy productivity. This paper presents an approach for automatically monitoring and recognizing behavior for dairy cows potentially relevant to thermotolerance, which utilizes various Machine Learning (ML) (e.g., CNN and YOLOv8) and Computer Vision (CV) (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency, brush use-behaviors, and social interactions such as headbutting. We evaluated the developed system using two different video datasets. The first dataset was captured using cameras installed at a side angle, while the second dataset was obtained using top-down camera views. Evaluation results showed that the YOLOv8 and CNN models achieved classification accuracies of 93% and 96%, respectively, on the first dataset, while the second dataset yielded 94% for YOLOv8 and 96% for CNN. Beyond experimental validation, the model is currently deployed for large-scale analysis of over 38,000 video recordings from a commercial ranch in Texas. This deployment enables automated extraction of behavioral event counts, specifically drinking and brushing, for each video, providing high-resolution behavioral summaries at scale. These findings show the robustness and operational viability of the system. The insights gained from this work hold significant promise for advancing genetic selection and management practices, equipping the dairy industry to proactively address heat stress and enhance overall herd resilience in the face of evolving climatic challenges.