The study focused on developing a machine learning (ML) model that efficiently estimates critical fuel properties like net heat of combustion (NHOC) and hydrocarbon density. Using the supervised support vector machines (SVM) algorithm, models with six and forty descriptors were trained for NHOC and density, respectively, ensuring accuracy and reliability. These models were then applied to screen molecules from literature and database, identifying 35 high-energy density polycyclic hydrocarbon (PCHCs) molecules suitable for sustainable aviation fuel (SAF) applications. Interestingly, around 70% of these PCHCs exhibited NHOCv values comparable to or better than JP-10 jet fuel. Notably, the optimal PCHCs favored multiple rings with C-C single bonds and a high H/C ratio. However, this prescreening step is just the beginning, as further steps involve developing quantitative structure-property relationships (QSPR), selecting or developing suitable catalysts for PCHCs synthesis, blending the PCHCs into aviation fuel, and assessing the impact on fuel properties according to ASTM specifications. Feasibility studies, techno-economic analyses, and environmental impact assessments are also crucial aspects of fuel development.