Scientists from Petroleum University of Technology applied advanced ML models including decision trees, random forests, and neural networks to estimate the density of binary cycloalkane blends in normal alkanes. The study, based on a robust dataset and sensitivity analysis, shows temperature as a major influence on density. Use these insights to refine fuel property evaluations.