Lung cancer is still a significant contributor to deaths from cancer worldwide, with millions of deaths annually and extremely low survival rates upon diagnosis at advanced stages. Traditional diagnostic methods, including imaging scans and biopsies, are frequently hampered by prohibitive cost, invasive testing, and subjectivity in interpretation, which postpones the initiation of treatment. By using big data to uncover subtle patterns that traditional methods might overlook, this study investigates how lung cancer early detection can be enhanced by machine learning. We investigated a few machines learning techniques such as neural networks, machine logistic regression, random forests, and support vector machines, to evaluate their capacity to differentiate between individuals with and without cancer. Data preprocessing, principal component analysis (PCA), and statistical correlation are used to select features; and an F1-s accuracy and AUC-ROC curves are used to assess the algorithm’s efficacy, precision, and recall; we establish the merits and demerits of each model. Random forest and neural networks outperform others, with the latter yielding the highest accuracy and reliability, with negligible false negatives. According to our research, chest cancer detection could be revolutionized by neural networks by improving diagnosis speed and accuracy, which will ultimately lead to greater patient survival rates. This study may be the foundation for the development of AI-assisted diagnostic software with seamless integration into existing medical practices, facilitating prompt medical attention and better patient results.