Machine learning (ML), in par�cular, emerges as a cost-effec�ve op�on for analyzing large datasets and predic�ng complex chemical proper�es by following the general steps in Fig. . By training models on molecular structures and corresponding descriptors, ML extracts paterns and rela�onships, facilita�ng the rapid screening of numerous fuel candidates. This predic�ve capability complements experimental measurements and computa�onally demanding quantum chemical calcula�ons, enabling the virtual construc�on of desired molecular structures and significantly reducing the �me and resources required for fuel screening. This rapid screening iden�fies the most promising candidates, saving significant �me and resources before future inves�ga�on can be done for NHOC (ASTM D4809) and density (ρ) at 15°C (ASTM D4052) and other alterna�ve fuel proper�es . The general process of ML is presented in Fig. .