Voice Activity Detection (VAD) is a crucial technology in the field of digital media, enabling efficient and effective audio processing by distinguishing speech segments from non-speech segments. This paper provides a comprehensive analysis of various VAD protocols used in digital media. The study delves into traditional methods, such as energy-based and zero-crossing rate approaches, and contrasts them with contemporary techniques that leverage machine learning and deep learning models. We examine the performance, accuracy, and computational complexity of these protocols, highlighting their respective strengths and limitations. The analysis includes a review of publicly available datasets and benchmarks used for evaluating VAD systems, offering insights into the current state of the art. Furthermore, we discuss the impact of different environmental conditions and noise levels on VAD performance, underscoring the challenges and advancements in robust VAD development. The findings indicate that while traditional methods are computationally efficient and easy to implement, they often fall short in noisy environments. In contrast, machine learning-based methods demonstrate superior accuracy and robustness, albeit at the cost of increased computational requirements. This paper aims to guide researchers and practitioners in selecting appropriate VAD protocols for their specific applications, fostering further innovation in the field of digital media.