Forecasting stock prices is a crucial aspect of informed financial decision-making and strategic investment planning. This research endeavors to elevate the precision of stock price predictions and decision-making by leveraging multilayered Bidirectional Long Short-Term Memory (BiLSTM) networks with Dynamic Indicator (DI) features, as proposed by J. Huang et al. [2]. The study conducts a comparative analysis between the predictive performance of conventional LSTM models and the innovative incremental BiLSTM models as discussed by D.Rajesh et al. [6]. These models incorporate a comprehensive set of both fundamental and technical analysis parameters, aiming to enhance overall accuracy and provide robust decision support. The research conducted by Zhang R et al. [5] has predominantly centered on the utilization of fundamental parameters and technical parameters are highlighted by D.Rajesh et al. [3]. Additionally, we incorporated key indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Price Rate of Change (ROC), and Average True Range (ATR), as these features have demonstrated potential in the accurate prediction of stock prices when employing LSTM models Binod Rimal et al. [8]. Despite the promising results, the inherent limitations of LSTM models in capturing intricate temporal dependencies have prompted the exploration of more advanced architectural frameworks. This paper introduces a novel methodology for developing Bidirectional Long Short-Term Memory (BiLSTM) model with DIF, as demonstrated for enhanced performance by Rishabh et al. [7], leveraging carefully chosen input variables. The performance of the proposed approach is assessed based on metrics such as Directional Accuracy, Hit Rate, and Quantile Loss. Experimental results reveal that the BiLSTM with DIF model outperforms the incremental BiLSTM model D.Rajesh et al. [6], demonstrating a superior fit to the data and achieving higher prediction accuracy.