The automatic identification of patients requiring psychiatric hospitalization is critical for ensuring clinical safety and optimizing resource allocation in emergency mental health services. This study presents a deep learning–based approach for binary hospitalization prediction from unstructured Spanish psychiatric clinical notes. A dataset of 500 emergency psychiatric evaluations collected from CAULE was curated and anonymized, resulting in 409 validated records after preprocessing and outcome filtering. Several Transformer-based architectures were evaluated, including general-purpose English models (BERT), Spanish-specific pretrained models (BETO), and a clinically oriented English model (ClinicalBERT). Models were fine-tuned under stratified 70–15–15 splits and further optimized through systematic hyperparameter search. Results show that Spanish-pretrained models achieve the best overall performance, with BETO-cased reaching 0.952 in both Accuracy and F1-score on the independent test set. Hyperparameter optimization significantly improves performance across architectures, particularly for language-aligned models. Findings highlight the importance of linguistic compatibility and optimized training configurations when applying Transformer models to psychiatric clinical text. This work contributes to advancing clinical NLP research in Spanish and supports the development of AI-driven decision-support tools for mental health care.