This chapter discusses the characterization, processing, and use of big data which is very popular these days in healthcare. Today medical data is increasing at fast pace where data is a deliberate asset that can be used to gain information. Most of the data is in an unstructured form. Modern developments in data gathering during periodic or repetitive health care, recorded as Electronic Health Records $($EHRs$),$ can be used for prediction. The safety and confidentiality concerns are associated with the data that is very big and available as medical records. In our proposed work, the data of pregnant ladies is collected with the help of wearable monitors and IoT devices that are stored in either xls or csv format. We considered basic features like the heartbeat of fetus, the uterine contractions etc. Basically analyzing it$,$ fetal well$-$being can be judged and fetus death rate can be decreased after predicting the suitable category. We have classified data into three categories, namely Normal, Suspect, and Pathologic, for the classification of significant data more prominently and accurately. Implementing various cases, using Random Forest, an accuracy of $93\mathrm{.}34\%$ is achieved $($without tuning the algorithm$).$ After applying linear regression and K$-$folds cross validation on the dataset, an accuracy of $96\mathrm{.}23\%$ is achieved $($with tuning the algorithm$).$ The model shows a good significant capability for prediction of fetal condition that would be helpful. :$"$ Discuss machine learning and how this could be applied to remote patient monitoring for patients in the Kingdom of Saudi Arabia. What challenges might biomedical researchers encounter when using these emerging technologies and analyzing the data gathered from them.Discuss machine learning and how this could be applied to remote patient monitoring for patients in the Kingdom of Saudi Arabia. What challenges might biomedical researchers encounter when using these emerging technologies and analyzing the data gathered from them. $"$