Cerebral palsy has affected millions of people worldwide. There are many causes that contributed to this condition, and it might happen during maternal, prenatal, perinatal, or postnatal life. Epidemiological studies have shown that most of the causes of cerebral palsy are prior to labour. However, there is no cure for this condition, and the diagnosis is still lacking in many aspects. Early assessments and interventions are crucial because they can improve patients’ health and quality of life. The purpose of this study is to predict the potential biomarkers for cerebral palsy by using bioinformatics approaches. Two datasets, GSE31243 and GSE11686, were downloaded from Gene Expression Omnibus (GEO) and GEO2R was performed to obtain the differentially expressed genes (DEGs). Enrichr, a gene set search engine was used to perform GO functional, enrichment analysis, functional annotations and KEGG pathway analysis for the DEGs. Protein-protein interaction (PPI) networks was constructed through STRING database and visualised by Cytoscape software. In total, 450 DEGs and ten hub genes were identified including LPL, LIPE, ACSL1, MT1E, MT1G, MT1X, MT1H, FABP3, PLIN2 and MT2A. In conclusion, by using bioinformatics approaches, several DEGs related to cerebral palsy were screened and the hub genes identified are crucial in differentiating cerebral palsy from other neurodevelopmental disorders.