The study presents a novel framework for remote consultation and lung and colon cancer classification, leveraging blockchain technology and Microsoft Azure cloud services to ensure data privacy and security. The framework achieves 100% accuracy in classifying lung and colon cancer using advanced machine learning models, demonstrating its potential to improve diagnostic accuracy and streamline cancer care. Key findings include effective cancer classification with high precision, recall, and F1-score, as well as enhanced data security through blockchain and Azure services. The implications of these findings include improved diagnostic efficiency and better patient outcomes, contributing to more effective cancer care and management. The framework integrates Azure's scalable cloud services with a permissioned blockchain network, ensuring secure and transparent data handling, anonymization, and controlled access. Advanced machine learning models, such as DenseNet, ResNet50, and MobileNet, are trained and deployed on Azure, achieving exceptional performance metrics. The study addresses the challenges of traditional diagnostic methods, including inefficiencies, high error rates, and data privacy concerns, by combining cloud computing and blockchain technology.The study presents a novel framework for remote consultation and lung and colon cancer classification, leveraging blockchain technology and Microsoft Azure cloud services to ensure data privacy and security. The framework achieves 100% accuracy in classifying lung and colon cancer using advanced machine learning models, demonstrating its potential to improve diagnostic accuracy and streamline cancer care. Key findings include effective cancer classification with high precision, recall, and F1-score, as well as enhanced data security through blockchain and Azure services. The implications of these findings include improved diagnostic efficiency and better patient outcomes, contributing to more effective cancer care and management. The framework integrates Azure's scalable cloud services with a permissioned blockchain network, ensuring secure and transparent data handling, anonymization, and controlled access. Advanced machine learning models, such as DenseNet, ResNet50, and MobileNet, are trained and deployed on Azure, achieving exceptional performance metrics. The study addresses the challenges of traditional diagnostic methods, including inefficiencies, high error rates, and data privacy concerns, by combining cloud computing and blockchain technology.