Effective Fetal Electrocardiogram (FECG) Extraction provides medical workers with precise knowledge for monitoring fetal health condition during gestational age. However, Fetal ECG Extraction still remains a challenge as the signal is weak and contaminated with noises of different kinds, more significantly maternal ECG. In this work, a new Moth Flame optimization algorithm (MFO)-based adaptive filter is proposed for the extraction of FECG signal. A noninvasive two-point method is used to record thoracic and abdominal ECG signals from the mother's body. The abdominal ECG (AECG) signal is made up of fetal heart signal, the distorted maternal heart signal and noise. The thoracic signal contains the undistorted maternal heart signal. The two signals are applied to an adaptive filter whose coefficients are optimally determined by the conventional least means square (LMS) algorithm and MFO. Simulation results using both synthetic signals and the real data from Physionet data base developed by MIT-BIH show the superiority of the new approach over conventional methods. The performance has been proven by observation of the quality of the extracted wave forms and quantitatively by computing the Signal to Noise ratio (SNR) which was 10.28 for proposed algorithm as compared to 0.1028 for the connectional LMS and mean square error (MSE) which was 0.0215 for the proposed algorithm as compared to 0.0275 for the convectional LMS. The results indicate that the new approach is suitable for Fetal Electrocardiogram extraction from AECG.