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The increasing prevalence of depression among young people is a growing global concern. Early detection and intervention are crucial, making the development of effective diagnostic tools essential. This work explores the use of advanced text and speech processing techniques to classify suicide risk within the context of the SpeechWellness Challenge (SW1) and verifies if speech can be used as a non-invasive and readily available mental health indicator. The analysis incorporated both linguistic features and audio-based methods for spontaneous speech and passage reading. For text classification, Large Language Models like Qwen2.5 and BERT were evaluated. For audio-based prediction, state-of-the-art speech processing models, including Whisper, Wav2Vec2 and HuBERT were employed. Furthermore, a multimodal approach combining both vocal and textual features was investigated. The results obtained in this research ranked among the highest in the challenge.