Accurately predicting calving time in cattle is crucial for enhancing livestock management and ensuring animal welfare. Our research presents a novel approach combining advanced computer vision and deep learning techniques to predict calving time based on cattle behavior. We employ a custom YOLOv8 model for cattle detection, achieving robust and efficient localization of individual cattle in various farm environments. Our Customized Tracking Algorithm (CTA) is utilized to maintain continuous identity tracking for each cow, allowing for precise monitoring of behavioral patterns. Feature extraction is performed using ResNet50, capturing detailed spatial characteristics of the detected cattle. These features are then organized into sequences to prepare them for temporal analysis. Finally, Custom Long Short-Term Memory (CLSTM) network is used for classification, leveraging the sequential nature of the data to predict the onset of calving with high accuracy. Our classification approach achieved an average accuracy of 94.88%. Our findings indicate promising performance from our CLSTM algorithm, accurately forecasting the remaining 3 h before calving. Through a comprehensive exploration of data collection, pre-processing, and feature engineering, our research paper establishes the foundation for training an accurate behavior model to predict calving time. Predicting calving using traditional, manual methods like observing breeding records and visual cues is complex and prone to errors, with even experts sometimes failing to make accurate predictions. Additionally, manual prediction becomes impractical and costly as farm size increases. Our automated system demonstrated a significant improvement in prediction accuracy, reducing false positives and providing timely alerts. Our proposed method demonstrates significant potential for improving the precision and reliability of calving time predictions, offering valuable insights for farm management and veterinary care.