This document summarizes a research paper that proposes a system called Carify to predict vehicle maintenance costs using artificial intelligence. Carify considers factors like mileage, vehicle age, fuel type, city and model to determine maintenance costs and the probability of parts needing replacement. It collects data from various sources and uses machine learning classifiers like Naive Bayes and decision trees to analyze the data and provide cost comparisons and predictions. The researchers believe such a system could benefit consumers, manufacturers, governments and used car buyers by estimating maintenance costs and health for different vehicles. They conclude that decision trees achieved the highest prediction accuracy and future work could involve collecting more data and using deep learning to improve forecasts.