This literature review examines the state of Text-to-SQL technology, which translates natural language queries into SQL. It analyzes rule-based, neural, and hybrid approaches, assessing their strengths and weaknesses, and surveys commonly used datasets, benchmarks, and evaluation metrics. The study identifies research gaps concerning generalization, scalability, and interpretability, and suggests integrating user feedback and domain knowledge. To better understand the implementation and potential improvements of machine learning in this domain, we conducted a systematic literature review (SLR) of publications from 2015 to 2023. From 439 gathered papers, 23 were identified as highly relevant. The review analyzes these works across four areas: (i) datasets employed, (ii) evolution of learning methods, (iii) development of evaluation procedures, and (iv) a meta-analysis of model performance. The findings confirm significant room for improvement in learning strategies. Persistent research gaps include cross-domain generalization, schema linking for complex databases, a lack of robust multilingual models, and the trade-off between model accuracy and interpretability. We propose future directions such as integrating contrastive schema linking, zero-shot/few-shot learning, explainability-driven design, and developing diverse, large-scale benchmarks that reflect real-world database complexity.