This paper addresses the persistent threat of Canine Parvovirus (CPV) to canine health, exploring a spectrum of outcomes from recovery to fatalities. Employing a fusion of machine learning techniques and comprehensive evaluation metrics, we present a robust analysis of CPV outbreaks. Our methodology involves the development of a deep learning-based predictive model designed to anticipate CPV case outcomes based on symptoms and diverse contributing factors, with performance monitoring through visualization techniques. The study delves into the intricacies of a dataset featuring diverse features such as age, breed, symptoms, treatment, and geographic location. Through meticulous preprocessing and feature encoding, we establish a powerful deep learning model proficient in discerning intricate patterns within the data. Model evaluation encompasses key metrics, including accuracy, precision, recall, F1-score, confusion matrix, Cohen’s Kappa, and Matthews Correlation Coefficient, providing a comprehensive assessment of predictive capabilities. Our findings highlight the model’s proficiency in anticipating CPV outcomes, suggesting potential enhancements in decision-making within veterinary practice. Insights derived from this research contribute to the refinement of CPV diagnosis, treatment, and prevention strategies, ultimately benefiting the well-being of canine companions. The project’s results demonstrate the efficacy of the proposed models in forecasting the prevalence and survival rate of the CPV virus in dogs using basic parameters. This approach eliminates the need for costly and time-consuming laboratory tests, typically requiring 12–24 h for results, showcasing a practical and efficient solution for CPV management.