The article reviews the applications of next-generation sequencing (NGS) in molecular ecology of non-model organisms, highlighting its potential to revolutionize ecological genomics. NGS has enabled the rapid and cost-effective generation of large-scale sequencing data, allowing researchers to study ecological, population genetic, and conservation genetic aspects of non-model species. The review covers various NGS applications, including transcriptome characterization, gene expression profiling, candidate gene identification, whole genome sequencing, targeted sequencing, large-scale molecular marker development, nucleotide variation profiling, and epigenetic studies. It emphasizes the importance of careful planning and data analysis, considering factors such as sequencing platform choice, read length, and computational resources. The authors also discuss the challenges and limitations of NGS, such as sequencing errors and the need for robust bioinformatics tools. Finally, they outline future prospects, including the potential for NGS to address more complex ecological and evolutionary questions in non-model organisms.The article reviews the applications of next-generation sequencing (NGS) in molecular ecology of non-model organisms, highlighting its potential to revolutionize ecological genomics. NGS has enabled the rapid and cost-effective generation of large-scale sequencing data, allowing researchers to study ecological, population genetic, and conservation genetic aspects of non-model species. The review covers various NGS applications, including transcriptome characterization, gene expression profiling, candidate gene identification, whole genome sequencing, targeted sequencing, large-scale molecular marker development, nucleotide variation profiling, and epigenetic studies. It emphasizes the importance of careful planning and data analysis, considering factors such as sequencing platform choice, read length, and computational resources. The authors also discuss the challenges and limitations of NGS, such as sequencing errors and the need for robust bioinformatics tools. Finally, they outline future prospects, including the potential for NGS to address more complex ecological and evolutionary questions in non-model organisms.