The article discusses the challenges and advancements in species delimitation, emphasizing the need for integrative taxonomy combined with artificial intelligence (AI) to address the complexities of evolutionary processes such as hybridization, polyploidy, and asexuality. Traditional species delimitation methods, primarily based on morphology, have limitations in accurately identifying species, especially in taxonomically complex groups (TCGs). The authors propose a unified species concept (USC) and an integrative taxon-omics approach that integrates genetic, morphological, and ecological data to improve species delimitation. They highlight the potential of AI, particularly machine learning (ML), to automate feature learning and data integration, reducing subjectivity in species delimitation. The article outlines the steps of an ML-based integrative taxon-omics approach, including data collection, transformation, and analysis using artificial neural networks (ANNs). It also discusses the importance of selecting diverse datasets, ranking and fusing data, and validating ML results by taxonomic experts. The authors conclude by emphasizing the need for increased standardization, automation, and public support to advance species delimitation and biodiversity research.The article discusses the challenges and advancements in species delimitation, emphasizing the need for integrative taxonomy combined with artificial intelligence (AI) to address the complexities of evolutionary processes such as hybridization, polyploidy, and asexuality. Traditional species delimitation methods, primarily based on morphology, have limitations in accurately identifying species, especially in taxonomically complex groups (TCGs). The authors propose a unified species concept (USC) and an integrative taxon-omics approach that integrates genetic, morphological, and ecological data to improve species delimitation. They highlight the potential of AI, particularly machine learning (ML), to automate feature learning and data integration, reducing subjectivity in species delimitation. The article outlines the steps of an ML-based integrative taxon-omics approach, including data collection, transformation, and analysis using artificial neural networks (ANNs). It also discusses the importance of selecting diverse datasets, ranking and fusing data, and validating ML results by taxonomic experts. The authors conclude by emphasizing the need for increased standardization, automation, and public support to advance species delimitation and biodiversity research.