Cancer continues to be a pervasive and alarming threat in today’s society, with lung cancer ranking among the most perilous forms of this disease. Fortunately, the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized the healthcare industry, offering promising avenues for lung cancer detection. In this paper, a comparative analysis of existing literature is conducted to provide a deeper understanding of the different approaches utilized in the lung cancer detection process. Drawing upon these findings, an innovative framework based on a decision support system (DSS) and IoT is proposed to facilitate faster and earlier detection of lung cancer to assist physicians in making informed treatment recommendations following the detection. By integrating IoT with a DSS, this paper advocates for improved early detection of lung cancer, leveraging insights from existing literature on machine learning (ML), deep learning (DL), and IoT-based diagnosis of lung cancer.