This paper presents a comprehensive literature review focusing on enhancing the security of Internet of Things (IoT) networks by integrating bio-inspired methodologies with Machine Learning (ML) and Deep Learning (DL) techniques. The authors selected 25 relevant studies from 145 published articles to address the research objectives. The findings highlight the efficacy of combining bio-inspired techniques with ML and DL approaches in improving the performance of Intrusion Detection Systems (IDSes), particularly in handling large datasets and reducing false alarms. The review also includes a comparative analysis of the selected articles, considering various factors, and outlines ongoing challenges and future directions in integrating bio-inspired techniques with ML and DL algorithms. The introduction discusses the importance of IDSes in protecting network integrity, confidentiality, and availability, and the need for more adaptive and sophisticated detection mechanisms in the evolving landscape of cyber threats. The paper references several related works, including surveys on feature selection methods, optimization algorithms, and intelligent intrusion detection techniques, to provide context and highlight the main differences from previous reviews.This paper presents a comprehensive literature review focusing on enhancing the security of Internet of Things (IoT) networks by integrating bio-inspired methodologies with Machine Learning (ML) and Deep Learning (DL) techniques. The authors selected 25 relevant studies from 145 published articles to address the research objectives. The findings highlight the efficacy of combining bio-inspired techniques with ML and DL approaches in improving the performance of Intrusion Detection Systems (IDSes), particularly in handling large datasets and reducing false alarms. The review also includes a comparative analysis of the selected articles, considering various factors, and outlines ongoing challenges and future directions in integrating bio-inspired techniques with ML and DL algorithms. The introduction discusses the importance of IDSes in protecting network integrity, confidentiality, and availability, and the need for more adaptive and sophisticated detection mechanisms in the evolving landscape of cyber threats. The paper references several related works, including surveys on feature selection methods, optimization algorithms, and intelligent intrusion detection techniques, to provide context and highlight the main differences from previous reviews.