This study presents a novel framework for remote consultation and classification of lung and colon cancer using blockchain technology and Microsoft Azure cloud services. The framework ensures data privacy and security while achieving high accuracy in cancer classification. The proposed system utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. The framework integrates Microsoft Azure's cloud services with a permissioned blockchain network, enabling secure data storage, access, and sharing. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. The framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios. The F1-score and k-fold cross-validation accuracy also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management. The framework addresses the critical issues of data privacy, security, and diagnostic accuracy in cancer care by combining cloud computing and blockchain technology. It provides a secure, efficient, and transparent solution for lung and colon cancer classification, improving diagnostic accuracy and patient outcomes. The study highlights the potential of integrating blockchain and cloud computing with advanced machine learning models to enhance cancer diagnosis and management.This study presents a novel framework for remote consultation and classification of lung and colon cancer using blockchain technology and Microsoft Azure cloud services. The framework ensures data privacy and security while achieving high accuracy in cancer classification. The proposed system utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. The framework integrates Microsoft Azure's cloud services with a permissioned blockchain network, enabling secure data storage, access, and sharing. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. The framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios. The F1-score and k-fold cross-validation accuracy also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management. The framework addresses the critical issues of data privacy, security, and diagnostic accuracy in cancer care by combining cloud computing and blockchain technology. It provides a secure, efficient, and transparent solution for lung and colon cancer classification, improving diagnostic accuracy and patient outcomes. The study highlights the potential of integrating blockchain and cloud computing with advanced machine learning models to enhance cancer diagnosis and management.