Financial markets are complex systems that use a vast quantity of data to generate a useful output. These details could range from basic data to any sociopolitical development which ultimately affects investor behavior. Precise market prediction becomes extremely challenging due to complex market systems and large-scale structural fluctuations. Machine learning tools use artificial algorithms to recognize patterns and provide extremely accurate price predictions in order to solve the forecasting challenge. Both asynchronous and non-uniform data can be captured by the machine learning tool, which is widely used as a forecasting tool. In order to lower the degree of bias, the study concentrated on increasing the complexity of the models by applying higher implications to the model. Both developed and developing nations are considered to comprehend the machine learning models’ accuracy. Based on previous research, the study further identifies a number of biases that could be accountable for the stock markets’ predictability in intraday trading. The study aims to connect investor behavioral biases with the stock market’s predictable trends.