Hypertension is a chronic condition that increases the risk of developing several causes, including cardiovascular diseases, cerebrovascular strokes, kidney failure, and hypertension attacks. Hypertension-related information must be tracked and evaluated in real time in order to mitigate these dangers. Real-time hypertension diagnosis and blood pressure (BP) monitoring are possible with Internet of Things (IoT) assisted health monitoring systems. In this paper, novel hypertension healthcare monitoring using deep learning network (HyCare-Net) technique in IoT has been proposed. The proposed method utilizing the Convolutional Neural Network for feature extraction to enhance classification accuracy. A Ghost Net classification technique classifies the extracted feature into three classes such as Low, Normal, High classes. Measures including specialty, f1score (F1S), accuracy, precision (PR) recall (RC) are used to assess the suggested approach. Compared to current models, experimental results using hypertension datasets show higher accuracy. The accuracy of the HyCare-Net approach in the hypertension dataset is 1.5%, 3.5%, and 7.5% higher than that of the current SB IOT-MPH, SPMR-DL, and IDOCNN techniques respectively