Rapid and accurate diagnostics for influenza viruses are essential for preventing future epidemics. Surface-enhanced Raman spectroscopy (SERS) presents a promising alternative to conventional techniques, offering a rapid, cost-effective, and highly sensitive platform for influenza virus detection. It is a highly sensitive analytical technique that enables the detection of minute chemical substances through significant signal enhancement. It operates by illuminating a sample with a laser and analyzing the scattered light to generate a unique molecular Raman spectrum. The sensitivity of SERS is derived from its use of metal nanoparticles, which amplify the weak Raman signals, making it particularly effective for detecting low-concentration targets such as viruses. Avian influenza (AI) is a major threat to domestic poultry, leading to large-scale culling during outbreaks. It leads to economic losses globally and can also infect pigs and humans, potentially causing a pandemic. Migratory birds spread various strains, leading to the development of highly pathogenic viruses. Viral monitoring is crucial for prevention strategies and understanding the virus evolution. This review outlines the challenges in detecting AI virus in chickens and critically assesses the established and emerging diagnostic technologies, with a specific focus on the factors influencing detection and recent advances in SERS-based AI detection. Ultimately, this review aims to provide insights that will assist the influenza research community in developing novel strategies for monitoring and preventing AI outbreaks in chickens and mitigating zoonotic transmission.