HRV analysis is an important non-invasive tool to assess the autonomic nervous system Intrauterine growth restricted (IUGR) individuals have greater predisposition to develop a metabolic syndrome in later life manifesting itself as obesity, hypertension, type 2 diabetes or cardiovascular disease. Poor fetal growth may alter the regularity mechanism of cardiac autonomic system that is involved in the development of these diseases. The malfunction-ing of the cardiac autonomic system assessed by decrease in heart rate variability (HRV) is associated with negative cardiovascular outcomes.The main objective of this work is to propose the best machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods; In this work, 41 IUGR (18 male) and 34 Non-I UGR (22 male) children were followed up 9 years after the birth, in average (9.1786 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes.This study is aimed at using AI algorithms to classify the normal and diseased children. The following AI algorithms were used; decision tree , Random forest, Support vector machine, neural network and LDA. They scored 83.95, 79.67, 80.87, 60.32, and 61.75 respectively. More development is required to improve the classification and the identification of Normal and diseased children.