In the exponentially evolving financial landscape predicting the stock prices is very incalculable which in turn results in unadvised decisions. Deep learning (DL) neural networks incorporated with predictive analytics have emerged as a critical tool for enhancing decision-making and risk management by identifying patterns and interdependence between temporal data using predictive modelling. By using historical data and past market behavior as well as real-time price fluctuations in prices deep learning algorithms can potentially forecast prices with a higher precision. This research presents an enterprise-grade state of the art predictive analytics system that synthesizes multivariate panel time-series data. This approach proposes a hybrid bidirectional Long Short-Term Memory (BiLSTM) and Wisdom of Crowd (WOC) architecture specifically packed together to understand the convolutions of market prediction, including the market volatility and intricate patterns in data. In the early instances the model trained on any random company is getting noteworthy performance using only a single dataset which provides more accurate and comprehensive financial predictions, surpassing traditional statistical methods. This research validates the robustness, scalability and accuracy of forecasting capabilities through rigorous comparative benchmarking of the 200 companies, delivering an exceptional (R2) average scoring of 0.9881, a Mean Absolute Error (MAE) of 0.0111, and a Mean-Squared Error-(MSE) of 0.00039. In order to estimate the impact and contribution of our new proposed model we benchmarked previous deep learning models in parallel with our newly proposed model leveraging a standardized dataset. Furthermore, we provide a feature analysis of our BiLSTM-WOC model providing insights into the unique impact of each individual component in extrapolating the stock prices. Our out of the box process puts a milestone and standard in stock market prediction presenting valuable insight.