Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders. C1 [Dhanabalan, Anantha Krishnan; Devadasan, Velmurugan] SRM Inst Sci & Technol, Sch Bioengn, Dept Biotechnol, Kattankulathur 603203, Tamil Nadu, India. [Haribabu, Jebiti] Univ Atacama, Fac Med, Carreras 1579, Copiapo 1532502, Chile. [Haribabu, Jebiti] Chennai Inst Technol CIT, Chennai 600069, Tamil Nadu, India. [Krishnasamy, Gunasekaran] Univ Madras, Ctr Adv Study Crystallog & Biophys, Guindy Campus, Chennai 600025, Tamil Nadu, India. C3 SRM Institute of Science & Technology Chennai; Universidad de Atacama; University of Madras