Stock price prediction has been a challenging task in financial markets. In recent years, the integration of natural language processing methods with machine learning algorithms has gained significant attention for its potential in extracting valuable insights from text data such as news articles. Classical machine learning algorithms often face exponential complexity with growing datasets and struggle with escaping local optima, while quantum machine learning techniques leverage quantum parallelism and interference effects to potentially offer exponential speedup and more efficient exploration of high-dimensional spaces. This chapter uses an approach combining the linguistic patterns from news articles headlines with cutting edge quantum machine learning techniques for stock price prediction. Textual features are extracted from news articles through Bag of Words, TF-IDF, and N-gram representations, aiming to encapsulate the semantic meaning and contextual variations of the articles. The integration of quantum techniques with textual features achieves superior prediction accuracy, representing the quantum machine learning importance in financial forecasting tasks. The experimental outcomes demonstrate the quantum random forest effectiveness in predicting stock prices compared to other quantum machine learning methods. Based on the outcomes, we observe that the quantum random forest method provides an accuracy of 96% and a precision of 97%. Furthermore, the study highlights the significance of leveraging textual data and advanced computational techniques for enhancing stock market prediction models, paving the way for more robust and accurate financial decision-making systems.