In recent years, ensuring public safety during live events has become a critical challenge due to the increasing scale of gatherings and potential risks. This study proposes a novel approach to Live Event Detection for People's Safety using audio data and the LightGBM classifier. The system leverages real-time audio streams to identify anomalies, such as loud disturbances, explosions, or unusual crowd behavior, which could indicate potential safety threats. Audio features are extracted using advanced signal processing techniques, including Mel-frequency cepstral coefficients (MFCCs), spectral contrast, and chroma features. These features are fed into a LightGBM classifier, which provides efficient and robust performance for real-time classification of event categories and potential risks. The proposed methodology is evaluated using diverse datasets comprising audio samples from live events, including concerts, sports, and emergency situations, to ensure a comprehensive understanding of normal and abnormal patterns. The LightGBM model demonstrates high accuracy, low latency, and scalability, making it suitable for deployment in real-time applications. Additionally, the system integrates a feedback loop for continuous model improvement based on new audio data. The results highlight the system's ability to enhance situational awareness and proactively alert authorities to potential risks, ensuring timely interventions. This approach demonstrates a significant step toward leveraging machine learning and audio analytics to improve public safety at live events.