Lung cancer is one of the most fatal cancers in the world for both genders. It has a high mortality rate compared to other types of cancer. Early detection can save lives and enhance the treatment process. As a result, the demand for approaches to detect cancer at an early stage is growing. In this paper, an Artificial Neural Network (ANN) model is developed to identify the level of having lung cancer based on environmental, diagnostic, and statistical factors. The features that highly affect the risk level of lung cancer were identified. The model's performance was assessed using a variety of criteria, including accuracy, precision, recall, and f-measure. Experimental results show that the model attains a high accuracy rate of 91.79% and risk factors like obesity, alcohol use, genetic risk, and coughing of blood can lead to lung cancer.