Prediction of higher heating value (HHV) using proximity and ultimate analysis is an important procedure for understandingthe characteristic attribute of a fuel. Researches put effort to model the relationship between the HHV value and those analyses.But conducted methods usually included only simple statistical analysis. In this paper we approach this prediction problemfrom the machine learning perspective, we employ four machine learning methods, i.e. linear regression, polynomial regression,decision tree regression and support vector regression to predict HHV using proximity and ultimate analysis of different typeof materials. Data set used is collected from literature and is categorized, where the resulting categories are used as features tobe fed to the machine learning models to create prediction models as accurate as possible. Performances of the proposedmethods are evaluated with k-fold cross-validation technique and each method’s pros and cons are discussed for both predictionaccuracy and computational complexity. Polynomial regression proved itself as the most optimal choice among others fromthese perspectives.