This paper presents advanced deep learning techniques for pancreatic cancer detection and classification from medical images with a specific focus on the hybrid architecture of InceptionDense. The combined paradigm retains the strengths of InceptionV3 and DenseNet121 through optimized convolutional channels with diversified scales in featured descriptions, which plays an influential role in the identification of tumors that vary in sizes and shapes. This dense connectivity in DenseNet121 promotes the reuse of features, hence enhancing the model’s capability to make differences in the middle of cancerous and non-cancerous cases. The study used a dataset of 1,411 annotated CT images obtained from Kaggle with balanced training and testing sets. Key hyperparameters included a fixed learning rate of 0.0001 with binary cross-entropy loss function and Adam optimizer, training over 10 epochs. Using accuracy, precision, recall, F1-score, specificity, and \(\text {R}^{2}\) score as evaluation metrics, InceptionDense had 99.75% accuracy while SSA with Stacked Deep Learning observed 99.26% only. Multi-scale feature extraction along with dense connectivity improved detection capabilities that were significantly high. This work aims to improve the diagnostic reliability of medical imaging. Its early diagnosis and treatment, aided by healthcare professionals, will actually improve outcomes for patients with pancreatic cancer. Future research will be focused on validating these methods with larger datasets and extending them to other imaging modalities, including MRI and ultrasound, in the hopes of furthering clinical applicability.