Silage preservation is critical for year-round livestock nutrition, yet spoilage from improper fermentation and aerobic deterioration causes annual losses exceeding 20% of ensiled biomass globally, compromising feed quality and farm profitability. Traditional sampling methods are labor-intensive, invasive, and provide only snapshots of heterogeneous fermentation processes, limiting early intervention. This review examines wireless gas sensor technologies and Internet of Things (IoT) architectures for continuous, real-time monitoring of silage quality through detection of key fermentation and spoilage gases including carbon dioxide, oxygen, ammonia, hydrogen sulfide, methane, and volatile organic compounds. Electrochemical, metal-oxide semiconductor, and optical sensor platforms deployed in wireless networks using LoRa, ZigBee, and NB-IoT protocols enable spatiotemporal mapping of gas dynamics within silos and bunkers. Cloud-based analytics and machine learning algorithms process multi-gas signatures to classify fermentation stages, predict spoilage risk, and trigger mobile alerts for timely management interventions. Case studies from European and North American dairy operations demonstrate substantial reductions in feed losses and improvements in nutritional consistency. Persistent challenges include sensor drift under harsh silo conditions, power management in remote deployments, and calibration maintenance. Future advances in edge computing, sensor fusion, and AI-driven predictive models promise scalable, cost-effective solutions for precision livestock farming, transforming silage management from reactive troubleshooting to proactive quality assurance.