Lung cancer is a global health concern, and early detection plays a crucial role in improving patient outcomes. In recent years, various computational approaches have been developed to aid in lung cancer prediction. This article presents a comprehensive comparative analysis of two popular machine learning techniques: Fuzzy Inference Systems (FIS) and Artificial Neural Networks (ANN). The aim is to evaluate their performance and suitability for lung cancer prediction. A dataset containing clinical and demographic features of lung cancer patients is used for experimentation. The analysis includes model training, evaluation, and interpretation, along with the incorporation of graphical figures for better understanding. The results shed light on the strengths and weaknesses of FIS and ANN in the context of lung cancer prediction, aiding researchers, and medical professionals in choosing the most appropriate technique for their applications.