Speech is the audible acoustic signal generated by the articulatory system (lungs, vocal folds, vocal tract, tongue, lips) to communicate language. The fundamental frequency (F_0) is the lowest frequency component of a speech waveform and corresponds to the vibration rate of the vocal folds during voiced speech. It also determines the pitch of the speaker’s voice. In speech signal processing, this acoustic waveform is captured and analyzed to extract information about what is being said, how it is being said and who is saying it. In various speech processing applications such as voice synthesis, speaker recognition and emotion analysis accurate extraction of the fundamental frequency (F_0) is a vital task. However, the vocal tract’s formants can sometimes significantly alter the glottal waveform’s shape, making it difficult to identify the true pitch. Additionally, in the presence of background noise, traditional pitch detection techniques often experience a considerable decline in performance. This work proposes a robust method for extracting the fundamental frequency by utilizing the complementary advantages of the power spectrum and logarithmic spectrum in noisy speech environments. The power spectrum mitigates noise effects, while the logarithmic operation effectively separates vocal tract characteristics from the source excitation. The proposed approach integrates autocorrelation-based power spectral analysis with cepstral techniques derived from the log spectrum to improve pitch estimation accuracy under adverse conditions. Experimental results on noisy speech datasets show that the proposed hybrid method achieves lower gross pitch error and greater robustness than traditional methods such as BaNa, autocorrelation and cepstral techniques.