Lung cancer detection constitutes a paramount process in the diagnosis and management of one of the predominant contributors of cancer-induced humanity worldwide. The significance of early screening is underscored by its essential role in enhancing survival rates through the identification of disease during a stage amenable to treatment. Diagnostic methodologies, including imaging modalities, are routinely utilized for diagnosis. Furthermore, advancements in the realm of molecular biology have facilitated the emergence of biomarkers and genetic examines, thereby enabling a more accurate identification of lung cancer. The prompt and defined detection of lung cancer facilitates timely therapeutic interventions, which significantly influence both the efficacy of treatment and overall outcomes for patients. The primary objective of this research is to develop an optimization-based hybrid deep learning methodology for the detection of lung cancer. The initial phase involves pre-processing of input images through techniques such as color space transformation, data augmentation, resizing, and normalization. Subsequently, features derived from Slime Mould Algorithm-based Convolutional Neural Network (SMA-CNN) are employed for the detection of lung cancer, with CNN being trained utilizing SMA, extracted from pre-processed images. Finally, the Squeeze-Inception V3 model, which integrates SqueezeNet and Inception V3, leverages SMA to train the classifier. Consequently, the proposed SMA-based hybrid SqueezeNet-Inception V3 is utilized to classify instances as normal or abnormal. Empirical results designate that SMA-based hybrid SqueezeNet-Inception V3 attained an accuracy of 97.3%, a specificity of 96.1%, and a sensitivity of 98%, thereby underscoring its efficacy in the detection of lung cancer.