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Conventional techniques are often unable to achieve the Fetal Electrocardiogram FECG extraction and R-peak detection in FECG from the abdominal ECG (AECG) in satisfactorily level for Fetal Heart Rate (FHR) monitoring. A new methodology by combining the Artificial Neural Network (ANN) and Correlation approach has been proposed in this paper. Artificial Neural Network is chosen primarily since it is adaptive to the nonlinear and time-varying features of the ECG signal. The supervised multilayer perception (MLP) network has been used because it requires a desired output in order to learn. Similarly, the Correlation method has been chosen as the correlation factor can be used to scale the MECG when subtracting it from the AECG, in order to get the FECG. By combining these two approaches the proposed methodology gives better and efficient result in terms of accuracy for FECG extraction and R-peak detection in the AECG signal due to its above characteristics. The proposed approach involves the FECG extraction from the AECG signal with the accuracy of 100% and R-peak detection performance is 93.75%, even though the overlapping situation of MECG and FECG signal in the AECG signal. Therefore the physician and clinician can make the correct decision for the well-being status of the fetus and mother during the pregnancy period.