Influenza viruses, notably influenza A (IAV), remain a persistent public health challenge due to high mutation rates and the rapid emergence of new strains. Wastewater-based epidemiology (WBE) offers a potential early warning system for monitoring the community-level circulation of viral diseases, including influenza. However, the use of next-generation sequencing (NGS) platforms for subtype identification from complex environmental matrices remains challenging. Here, we evaluated Oxford Nanopore Technologies (ONT) and Illumina sequencing platforms utilising the Illumina’s Respiratory Virus Oligo Panel (RVOP) and Twist’s Respiratory Virus Research Panel for detection and characterisation of IAV from wastewater (native and spiked samples) and environmental samples. Sequence data were assessed for read quality, mapping efficiency, and segment coverage. ONT consistently recovered all eight influenza segments across a range of concentrations, demonstrating robustness in low-viral-load scenarios. Illumina performance was sensitive to sample composition and concentration with improved detection in subtype-dominant samples. Among Illumina approaches, the Twist Panel achieved almost complete segment recovery. We document a workflow using granular activated carbon, recovering of all eight segments (with >99.6% genome coverage) from wastewater and environmental samples. These findings document successful IAV subtyping from complex environmental matrices highlighting the importance of sampling optimisation for influenza surveillance using WBE. Integrating complementary sequencing approaches enhances subtype resolution and strengthens