Health monitoring systems are improving with the development of the internet of things. This paper proposes a secure architecture consisting of a four-layer internet of things enabled health monitoring system that collects patient data and classifies them into different medical categories. While collecting patient information from their wearable smart sensing devices for computation, the privacy and security of this process are essential. The main motive of this paper is to develop a lightweight and secure communication protocol using blockchain architecture for decentralized IoT networks and classify them into different categories using transfer learning. We propose a framework that uses blockchain for security and incorporates transfer learning to use multiple pre-trained models. The proposed routing technique uses factors like probability, credibility rating, and node energy to route the data to its destination such that the network overhead is reduced and the energy used is minimal. We classify the collected patient information using four different pre-trained convolutional neural network models: ResNet50, VGG19, InceptionV3, and SqueezeNet. We simulate the proposed routing approach and other benchmark schemes on various performance metrics. The results show that the proposed approach gives 92.24% classification accuracy.