Lung cancer is one of the most devastating malignancies, which shows the critical importance of the necessity for early, accurate detection, and effective treatments. This study discusses the joining of ant swarm intelligence and deep learning and its implementation in lung cancer diagnostics and treatment. The following models are analyzed: particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC), and hybrid configurations of Convolutional Neural Networks (CNNs). They are evaluated against criteria, like detection efficiency, accuracy, time complexity, and computational demands, to make them applicable. The integration of swarm intelligence methodologies with deep learning models is revealed as a good way to occupy empty niches on the market by the accuracy of diagnosis and novel drug design through computational calculations. Besides the significant improvement in diagnostic precision and drug delivery system optimization, the integration of swarm intelligence techniques with deep learning models in fact allows the new medication development and results of surgeries of better quality due to the effect of two technologies on this area. This review gives the actual state of research, determines the main difficulties such as computational overhead, and puts forward some possible solutions to improve system performance. The results wrap up with perspectives on how the future research should develop, concentrating on the potential of interdisciplinary collaborations to increase technology use in the clinical domain.