Predicting stock market prices and trends is challenging and complex because of its volatile and chaotic nature. Earlier or last-century studies relied on statistical techniques for prediction, which are typically linear and have minimal parameters. The off-late stock environment is based on heterogeneity and complexities, leaving the traditional prediction models to be updated. Stock market prices are determined not merely by financial variables but are primarily influenced by the global political environment. Forecasting is determined by the current climate and performance, expected developments in the financial and technological sectors, and unpredicted variables. Several deep learning architectures, techniques, and tools have been explored, both independently and in combination, yielding improved predictive performance. This paper captures the underlying dynamics of the stock market through extensive studies leveraging machine and deep learning techniques. Additionally, it highlights the persistent challenges prediction models face in addressing the complexities and uncertainties of financial forecasting.