This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for predicting different diseases, using machine learning algorithms, has been done. The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT. Each technique was analyzed on each step, and the overall drawbacks are pointed out. In addition, it also analyzes the type of data used for predicting the concerned disease, whether it is the benchmark or manually collected data. Finally, research directions have been identified and depicted based on the various existing methodologies. This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws.