Pregnancy is the period in which the female uterus carries a developing fetus. Labor is the detection of uterine contraction activity, which is monitored using various instruments such as ultrasound, IUP Cand, and tocodynamometry. However, the device or instrument is not an excellent estimation of the term or preterm birth date. We now have a low-accuracy device for monitoring maternal cases, which includes labor diagnosis and delivery prediction. It is difficult to make medical treatment decisions, including tocolytic therapy, the administration of steroids, and admission or transport to a hospital. There are many factors or conditions that are associated with the risk of preterm labor which includes stress, bleeding during pregnancy, and chronic conditions. In this study, shows that the maternal condition, which is the bleeding of the woman during pregnancy (early delivery), is the main factor or indicator of preterm labor, as well as, we have shown another alternative that is currently used in human monitoring techniques is uterine electro hysterography (EHG) or electromyography (EMG), this system Use an Ag/AgCl electrode between the above and below navel to record data. We stand by the clinical information of the EHG data set to demonstrate that this maternal condition is used for preterm and term labor prediction and analysis only in the first and second trimesters for preterm EHG signal features. To implement this work, it uses the TPEHG dataset from Physio Net, which contains only the first and second trimester maternal bleeding electro-hysterogram records obtained during regular examinations of pregnant women in the Department of Obstetrics and Gynecology of the Medical Center at the University of Ljubljana. by using machine learning algorithm for prediction labor, for preprocessing that utilizes a filter (Butterworth, IIR notch, Savitziky-Golay), and for feature extraction, (wavelet transform) and ten feature is extracted which is SSI, RMS, crest factor, variance, median frequency, mean power, peak to peak amplitude, peak frequency and sample of entropy. After this, we have use classifiers for classification, which are support vector machines (SVM), k-nearest neighbor (KNN), Decision tree (DT) and Random Forest (RF). We have achieved a result of SVM 84.85%, DT 81.82%, RF 84.85% and KNN 81.82% accuracy. Keywords: Electro hysterogram, Feature extraction, Labor, Machine Learning, Preterm birth, Support Vector Machine, Term birth, Wavelet Transform