The primary objective of this research is to create a model for spelling correction of texts in the Azerbaijani language. This involves the use of an LSTM-based Seq2Seq model architecture with an attention mechanism. The study follows a comprehensive methodology that covers data collection, model development, evaluation, and implementation. A robust dataset of 92,500 sentence pairs, primarily gathered from social media platforms such as YouTube, provided a diverse and representative sample of real-world language use. This dataset was instrumental in training the model, which was designed with Encoder-Decoder layers and an attention mechanism to handle the complexities of Azerbaijani language morphology and syntax. The system’s accuracy and effectiveness were evaluated by comparing its performance against Mirza.az, an existing Azerbaijani language correction tool. The model demonstrated a significant improvement, achieving 97.4% accuracy on the test dataset, showcasing its potential as a reliable tool for spelling correction. This research contributes to the advancement of Azerbaijani language processing tools, aiming to enhance digital communication, linguistic accuracy, and academic writing standards in Azerbaijani.