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Hypertension is an epidemic restricted not only to the developing but also to the developed nations. It is triggered by various lifestyle choices that depend on each individual based on their personal physiology and lifestyle. Early diagnosis is possible, but it requires continuous blood pressure monitoring. Various machine learning methods have been proposed for early diagnosis of hypertension by predicting blood pressure and detecting high spikes in the values. However, these solutions are built upon the generic guidelines which may not be applicable for every patient. Most of these solutions incorporate batch learning and require all data to be present before prediction and do not support any online learning mechanism. This leads to potentially outdated models. Furthermore, there is also a lack of an intelligent approach to handling incomplete time series while training the model. This paper presents a personalized approach to estimate blood pressure that eliminates the need for continuous monitoring based on the Online recurrent extreme learning machine (OR-ELM). The missing values are imputed using Gaussian mixture models. The prediction model learns from the historical data and learns online as more data becomes available. The proposed scheme is developed and deployed on a mobile application for secured prediction results. The method is used to predict blood pressure in Malaysian population and compared with existing batch-learning and online learning methods. The results show that OR-ELM based model outperforms the existing online techniques such as the Online sequential extreme learning machine and batch learning technique such as Extreme learning machine.