*Result*: Accelerating promoter identification and design by deep learning.

*Engineered promoters enable precise modulation of recombinant protein expression and metabolic pathways, facilitating natural product biosynthesis and biotechnological applications. Deep learning techniques are transforming the field by enabling accurate promoter identification, strength prediction across species, and de novo design through generative models. Combining generative models with predictive networks enables rapid generation and testing of large numbers of potential promoter designs. This accelerates the development process and enhances the precision of promoter engineering. Database quality, feature extraction, and model architecture are key factors that significantly impact the accuracy and reliability of deep learning models in promoter engineering. Promoters are DNA sequences that govern the location, direction, and strength of gene transcription, playing a pivotal role in cellular growth and lifespan. Engineered promoters facilitate precise control of recombinant protein expression and metabolic pathway modulation for natural product biosynthesis. Traditional methods such as rational design and directed evolution have established the foundation for promoter engineering, and recent advances in deep learning (DL) have revolutionized the field. This review highlights the application of DL techniques for promoter identification, strength prediction, and de novo design using generative models. We describe how these tools are used and the impact of database quality, feature extraction, and model architecture on predictive accuracy. We discuss challenges and perspectives in developing robust models for promoter engineering. [ABSTRACT FROM AUTHOR]*