This paper presents a systematic literature review exploring the intersection of employee burnout and speech analysis, proposing a conceptual multidimensional framework for future predictive modeling. The review adheres to the PRISMA 2020 guidelines and includes searching scientific databases, such as Scopus and PubMed, selecting and summarizing studies linking burnout, including the Maslach Burnout Inventory, and various digital phenotyping elements, such as acoustic-prosodic parameters, speech emotion recognition, and natural language processing results. Addressing the identified methodological gaps, this study outlines a theoretical framework that integrates self-supervised speech representations—specifically models like wav2vec, HuBERT, and WavLM—with emotional features, text indicators, and Organizational Network Analysis to inform future management systems. Model portability, data quality, and practical applicability are discussed separately, including cultural and linguistic specifics and personal data protection requirements in Kazakhstan (e.g., the personal data law and privacy governance approaches). The synthesized findings highlight the potential and limitations of current speech-based AI, providing a roadmap for developing ethically sound, context-aware systems for early burnout detection and preventative interventions.