This thesis details the technical contributions of a thesis focused on advancing Spoken Language Understanding (SLU) through the application of modern State Space Models (SSMs). The core objective was to investigate, implement, and validate SSM-based architectures (specifically S4 and Mamba) as an efficient and powerful alternative to mainstream models for intent classification on edge devices. This work spans foundational experimentation, deep integration into major open-source toolkits, and the development of a novel end-to-end SLU system. Contributions were made across three key codebases: S4, for initial prototyping and validation on the Fluent Speech Commands (FSC) dataset; ESPnet, for community-facing integration and bug resolution; and SpeechBrain, which houses the final, optimized end-to-end Mamba-based SLU models.