This paper introduces SLURP-TN, a comprehensive spoken language understanding resource specifically designed for the Tunisian dialect. The dataset comprises 4165 sentences recorded from 55 native speakers, totaling approximately 5 hours of acoustic material. By translating sentences from six SLURP domains, the authors address the critical gap in SLU resources for low-resource languages. The research develops baseline Automatic Speech Recognition and SLU models that leverage deep neural networks and pre-trained language models to extract semantic information from speech utterances in task-oriented dialogue systems. This work enables the Tunisian-speaking population to benefit from recent advances in natural language processing and speech recognition technology.