This study presents a deep learning-based approach for detecting cyberbullying in the Kazakh language, addressing key challenges associated with low-resource languages. The research highlights the increasing prevalence of cyberbullying in Kazakhstan, the limitations of traditional machine learning models, and the need for advanced text classification techniques. A novel hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer-based architectures is proposed to enhance detection accuracy. The study details the dataset collection process, data augmentation techniques, and model evaluation using key performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed model significantly outperforms conventional machine learning algorithms and previously published methods. The findings offer practical implications for automated content moderation on social media platforms and contribute to the advancement of natural language processing (NLP) tools for the Kazakh language.