The main objective of the web system is to generate the diagnosis of canine viral diseases using artificial intelligence techniques to support the clinical history processes of veterinaries. This system aims to streamline the processes of medical records management, appointment arrangement and, mainly, the diagnosis of canine viral diseases. The system evaluates the animal’s symptoms to support the veterinarian’s decision-making and make a more accurate diagnosis. The information provided by the system is useful for treating rabies, measles, and parvovirus. The CRISP-DM methodology ensured a structured and systematic approach to the development of the system, ensuring that the objectives and requirements of the project were met. In the deployment and visualization of the data, a web system developed in PHP was used, integrating the learning models with PHP-ML and decision trees to map the conditions to rule structures and create a hybrid system that combines machine learning with rule-based reasoning.