This generation's leading cause of cancer-related death is lung cancer, and it is anticipated that this will continue for the foreseeable future. Lung cancer can be successfully treated if the disease's early signs are recognized. Deep learning (DL) models based on LSTM, RNN and CNN were utilized to optimize the process of detection from the lung cancer dataset. Lung cancer patients are categorized according to their symptoms using DL classifiers, and the Python programming language is also used to advance the model's implementation. Accuracy, precision, and recall were just a few of the different metrics used to assess how good our DL models were. The suggested LSTM model was contrasted with the RNN and CNN techniques already in use. Comparing the recommended LSTM method to the current ones, it achieves a rate of accuracy of 93%.