The nature that is complex and volatile of market has made it difficult to accurately determine the value of stocks. The work present a mixed approach which integrates sentiment analysis with Long Short-Term Memory alongside BERT architecture for stock price prediction purposes. Long Short-Term Memory and BERT architecture in improving stock price prediction methods. By considering quarter earnings call transcripts as the vocalization of market analysts and including investors sentiment subtleties, BERT performs remarkably well in emotion detection. This is combined with historical stock data in an LSTM network to explain temporal behavior patterns in investor sentiments. The model utilized impactful images for predicting future stock prices complemented by explanations that make understanding the forecasted trends easier. The work spotlights the significance of NLP and deep learning techniques in analyzing the financial statements and knowing their significance within the market sectors.