ASD is a group of complex Neurodevelopmental disorders characterised by social interaction and communication deficits, as well as repetitive and stereotyped behaviours. Because the disorder's aetiology and pathogenesis are unknown, there is no specific treatment or reliable diagnostic biomarkers. Early behavioural interventions have been shown to significantly improve symptoms in ASD children. Given the rapidly increasing prevalence of ASD, identifying related diagnostic biomarkers is critical. Although specific diagnostic markers for ASD have not been identified, related research has advanced in various areas. This review summarises recent research on the use of genes, proteins, peptides, and metabolites as diagnostic markers for autism spectrum disorder. Genetic testing, as well as proteomic and metabolomics analyses, are examples of associated techniques. Furthermore, some studies have concentrated on single or multiple proteins and metabolites. Transcriptomic analysis, immune disruptions, and cytokine levels may also be used for this purpose. Autism spectrum disorder (ASD) is a diverse group of Neurodevelopmental disorders characterised by impaired social interaction and communication, as well as repetitive and stereotyped behaviour. ASD is defined as autistic disorder, Asperger syndrome, childhood disintegrative disorder, and pervasive developmental disorders not otherwise specified (PDD-NOS) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). ASD affects about 1-2% of the population, with a male to female ratio of 4-5:1. According to a recent meta-analysis of twin studies, the heritability estimates for ASD ranged from 64-91%. Two additional studies found that ASD has a heritability of around 80%, and a study found that social impairments associated with ASD have a heritability of 60.9%. These findings imply that genetic factors may be important in the development of ASD. Furthermore, the role of genetic factors in ASD risk varies greatly and is complex. The advantage of metabolism-based analysis is that it is sensitive to interactions between the genome, gut micro biome, diet, and environmental factors. These factors contribute to an individual's unique metabolic signature, which has great potential for the diagnosis and prognostic evaluation of neuropsychiatric diseases such as ASD.  MS and nuclear magnetic resonance (NMR) spectroscopy are two analytical platforms for no targeted metabolomics and targeted metabolomics. Transcriptomic studies can shed light on the various ASD-causing genetic mechanisms or risk factors that may result in common behavioural outcomes. Whole-transcriptome analysis is now possible thanks to microarrays followed by high-throughput RNA sequencing (RNA-seq). These methods provide information on transcript abundance and can be applied to mRNA as well as small and long noncoding RNA (lncRNA) transcripts. Data from RNA-seq can also be used to investigate the nature and frequency of alternative splicing.