Electronic Nose (E-Nose) systems have emerged as powerful analytical tools engineered to mimic the mammalian olfactory system, offering rapid, non-invasive, and cost-effective alternatives to traditional chemical analysis methods. Given the vast and multifaceted nature of recent research, this paper presents a detailed and multidimensional analysis of the primary components dictating E-Nose efficacy: target analytes, gas sensors, and Machine Learning (ML) algorithms. Based on a structured review of literature published between 2019 and 2025, we quantitatively explore the interdependencies among these components to identify key trends and performance drivers. Our statistical analysis reveals a continued reliance on Metal Oxide Semiconductor (MOS) sensors due to their cost-effectiveness, alongside a significant paradigm shift from traditional statistical methods toward Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNNs) and hybrid models, which are increasingly utilized to handle high-dimensional sensor data. Furthermore, we provide a granular review of applications across major sectors, including Food and Beverage, covering quality assessment, fraud detection, and flavor characterization, as well as Healthcare, and Environmental and Agricultural Monitoring. Finally, this survey critically evaluates persistent challenges such as sensor drift, detection performance, data scarcity, and explainability, while outlining emerging research opportunities in novel nanomaterials, IoT integration, Edge Computing, and multi-modal sensing to facilitate the transition from laboratory prototypes to robust commercial applications.