With the rapid proliferation of wireless communication systems, accurate Start-of-Packet (SOP) detection is essential for efficient packet reception and decoding. Conventional correlation-based techniques struggle under low signal-to-noise ratio (SNR) and phase/frequency offsets. This paper proposes a Proximity-based Support Vector Machine (P-SVM), a novel SVM-based approach that incorporates Proximity-based weighting and a Distance adjustment function to enhance robustness under such impairments. Evaluated using both simulations and SDR-based experiments, P-SVM consistently outperforms conventional and state-of-the-art methods in Probability of detection, Mean Absolute Error, and Probability of false alarm. Moreover, P-SVM offers reduced implementation complexity, making it suitable for practical deployment.