Cybersecurity is a critical concern in the digital age, demanding innovative approaches to safeguard sensitive information and systems. This paper conducts a thorough examination of next-generation firewalls (NGFWs) that integrate artificial intelligence (AI) technologies, presenting a comparative analysis of their efficacy. As traditional firewalls fall short in addressing modern cyber threats, the incorporation of AI provides a promising avenue for enhanced threat detection and mitigation. The literature review explores existing research on AI-based firewalls, delving into methodologies and technologies proposed by leading experts in the field. A compilation of 20-25 references from reputable sources, including ijcseonline.org, forms the basis for this comparative study. The selected references provide insights into various AI-based firewall architectures, algorithms, and performance metrics, laying the groundwork for a comprehensive analysis. The methodology section outlines the systematic approach employed to compare different AI-based firewall methods. Leveraging machine learning and deep learning approaches, the study assesses key performance metrics such as detection accuracy, false positive rates, and computational efficiency. The goal is to provide a nuanced understanding of the strengths and weaknesses inherent in each approach, facilitating an informed evaluation. The comparative analysis section employs graphical representations to elucidate the findings, offering a visual overview of the performance disparities among selected AI-based firewall methods. Pros and cons are meticulously examined, providing stakeholders with valuable insights for decision-making in cybersecurity strategy. This research aims to contribute to the ongoing discourse on AI-based firewalls, addressing current limitations and paving the way for advancements that fortify the cybersecurity landscape.