Since this is a main issue in healthcare, new approaches are required to increase the accuracy of cancer diagnosis and the results for patients. “This paper proposes a hybrid artificial intelligence framework combining several AI approaches like rule-based systems, deep learning, and machine learning to improve cancer detection across several modalities, including genetic data, radiological scans, and histopathological images. While addressing issues such imbalanced datasets, overfitting, and interpretability, the framework makes use of the capabilities of particular approaches, such ensemble methods for robustness, convolutional neural networks (CNNs), and support vector machines (SVMs). Current augmentation techniques preprocess the data to guarantee diversity and lower noise. They then cross-evaluate the model after optimizing it with hyperparameter changes. Area under the curve (AUC-ROC), recall, accuracy, precision, and F1-score all show rather notable increases when compared to stand-alone methods. Using explainability systems, case studies show the model may effectively detect early-stage malignancies, lower false positives and negatives, and offer insightful analysis. These results suggest that hybrid artificial intelligence systems could drastically enhance cancer detection, therefore enabling more customized treatments and better quality of living for patients. As researchers investigate how to combine wearable devices, real-time applications, and scalable frameworks to identify several kinds of cancer, more easily available and comprehensive healthcare solutions will be feasible.