The landscape of cybersecurity has undergone drastic changes in recent years, largely due to the emergence of increasingly complex cyber threats. This paper provides a comparative review of the advantages and disadvantages of AI-based and conventional Intrusion Detection Systems (IDSs), which are software applications used to monitor network or system activities and detect whether they are under attack by viruses, malware, ransomware, or other malicious threats. Traditional IDS has faced a significant challenge for many years, as unknown attacks have continued to occur, despite various approaches proposed to enhance the efficiency of IDS. Despite applying proper measures and secured configurations, many attacks, threats, and malicious activities remain undetected. AI solutions utilize Machine Learning (ML) and Deep Learning (DL) algorithms to enhance detection capabilities and adapt to evolving threats. This review indicates several intrusion detection software schemes. To assist organizations in choosing a suitable IDS. Also consider the necessary selection criteria for organizations evaluating intrusion detection, including the need for a custom approach that can be tailored to their specific requirements