Stock market or individual stock forecasting poses a significant challenge due to the influence of uncertainty and dynamic conditions in financial markets. Traditional methods, such as fundamental and technical analysis, have been limited in coping with uncertainty. In recent years, this has led to a growing interest in using deep learning-based models for stock prediction. However, the accuracy and reliability of these models depend on correctly implementing a series of critical steps. These steps include data collection and analysis, feature extraction and selection, noise elimination, model selection and architecture determination, choice of training-test approach, and performance evaluation. This study systematically examined deep learning-based stock forecasting models in the literature, investigating the effects of these steps on the model’s forecasting performance. This review focused on the studies between 2020–2024, identifying influential studies by conducting a systematic literature search across three different databases. The identified studies regarding seven critical steps essential for creating successful and reliable prediction models were thoroughly examined. The findings from these examinations were summarized in tables, and the gaps in the literature were detailed. This systematic review not only provides a comprehensive understanding of current studies but also serves as a guide for future research.