Lung cancer is a fatal disease with a high mortality rate in patience. Early and accurate detection of this disease plays a crucial role in improving a patient's chances of survival. Traditional methods, such as Computed Tomography (CT) scans, have historically been employed for tumor localization and assessing cancer severity. However, these methods are time-consuming and may pose risks, including patient mortality before tumor identification. Given the challenges associated with lung cancer classification and the limitations of existing practices, there is a pressing need for innovative clinical data assessment tools to complement biopsies and offer a more precise characterization of the disease. Recent literature suggests the application of deep learning techniques for lung cancer detection. However, efficient training of deep learning models requires a substantial amount of data, and the availability of annotated data for lung cancer detection is often constrained, potentially resulting in overfitting or under-fitting issues and inaccurate predictions. To address these challenges, this dissertation proposes a novel deep learning architecture based on the hybridization of three pre-trained models with a support vector machine (SVM) optimized using the honey badger optimization algorithm (HBA). The process involves pre-processing the input images to ensure compatibility with pre-trained models, implementing augmentation techniques to expand the dataset and prevent overfitting, and employing a hybrid model consisting of AlexNet, VGG16, and GoogleNet for feature extraction. The extracted features are combined to generate hybrid features, which are then fed into a multi-class SVM optimized with HBA for classification. The proposed model was trained and tested using a lung cancer dataset from Iraq-Oncology Teaching Hospital and the National Centre for Cancer Diseases (IQ-OTH/NCCD), comprising 1190 images across three categories: normal, benign, and malignant. The model underwent validation and was compared with existing literature works. The results demonstrated superior performance, achieving an overall accuracy of 98% in accurately detecting different categories of lung cancer. This result demonstrates the capability of the proposed model compared to other existing models from the literature.