Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat, necessitating advances in medical imaging techniques for early and accurate detection. This study presents a novel hybrid approach to pancreatic cancer histopathology image segmentation by integrating deep neural networks with traditional machine learning models. Our method leverages the strengths of both paradigms to enhance segmentation performance. Specifically, we employ supervised learning to train deep convolutional neural networks (CNNs), namely ResNet50 and VGG16, to extract high-level feature vectors from medical histopathology images obtained from The Cancer Imaging Archive pancreatic-ct dataset by the National Institutes of Health Clinical Center. These feature vectors serve as inputs to various machine learning classifiers, including Random Forest, K-Nearest Neighbors (KNN), XGBoost, Linear Support Vector Machine (LinearSVM), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB). Combining the feature extraction capabilities of deep learning models with the decision-making prowess of traditional classifiers, our hybrid framework based on XGBoost produced the best segmentation results among other classifiers by achieving a precision value of (0.96), F1-Score value of (0.97) and a recall value of (0.98). Extensive experiments and cross-validation on benchmark datasets demonstrate that our approach outperforms standalone models, showcasing its potential in clinical applications for improved diagnostic accuracy (0.936) and patient outcomes.