Edge computing allows the analysis of data close to the sources of its generation. This computing paradigm has enabled multiple avenues in different types of applications with the usage of Artificial Intelligence. Smart remote health monitoring is one such application that requires medical data analysis with the help of AI. In this paper, a methodology for blood pressure estimation from Electrocardiograph data using Machine Learning (ML) techniques is proposed that can be run on resource-constrained devices e.g., wearable devices. The proposed methodology requires only ECG data that can be acquired in a non-invasive manner. Experimental results show that the proposed methodology is able to achieve better results compared to similar techniques proposed in the literature.