Stock price prediction owes much to sentiment analysis and AI, which offer a holistic view of market sentiment and pattern detection in massive datasets. Through sentiment analysis, textual data is processed from various sources, such as social media, news articles, and financial statements, to capture the public's mood toward a company or the market in general. Such insights are then used to develop an understanding of stock price movements from the perspective of investor behaviour and market trends; this gives credence to traditional modes of analysis. Machine learning algorithms and neural networks in AI serve to analyze the vast datasets, finding previously unknown patterns, thus enhancing the prediction of stock price values. AI can sift through enormous volumes of historical price data, economic indicators, and other information of pertinent interest to an analyst. Identification of complex patterns and interrelationships among the data could potentially enhance the accuracy of predictions. Adaptability is where AI scores above any traditional mechanism. Design/Methodology/Approach The design involves a comprehensive approach for the study of the effects of sentiment analysis and AI on stock price predictions. First, it involves the collection of an integrated dataset of historical stock prices, social media posts, news articles, and financial reports. It then involves the development of various sentiment analysis models such as natural language processing (NLP) algorithms for interpreting data, classifying, and quantifying the sentiment of the expressed text. The sentiment analysis model needs to be first trained on labeled data so that it can identify sentiment on different text data into three likely classes-positive, negative, or neutral. After that, the models employing AI, including machine learning and deep learning algorithms, are used to analyze correlations and relationships of sentiment data with stock price movements, targeting the learning of future patterns and correlations from input as sentiment data for the prediction of future stock prices. The last step entails comparing the performance using metrics such as accuracy, precision, and recall and contrasting the results against outdated models for stock price predictions. Findings Research conducted recently on sentiment analysis and artificial intelligence about stock price prediction gave some major findings. AI models, particularly those that have a link to sentiment analysis, increased the predictive accuracy beyond traditional models, and hybridized models using classical machine-learning techniques and state-of-the-art deep learning technologies, such as LSTM networks and gated recurrent units, have performed excellently. This is because sentiment analysis is a method whereby textual data from news articles and social media sources are converted into sentiment scores-that is transformed into simple numbers from complex emotions-to improve the predicative ability of the models on capturing the emotional bias in the market towards the events. Some of the performance metrics that have even proven how AI models can be superior in stock price prediction applications include its accuracy, precision, recall, F1 score, and ROC AUC. However, complex models like FinBERT might not be the best choice overall compared to the simpler model Logistic Regression in lighting efficiency versus accuracy. Therefore, continuous monitoring and updating of models with new data are essential to their relevance and accuracy over time. Originality The innovation of utilizing sentiment analysis and AI for forecasting stock price is the interesting amalgamation of modern-day natural language processing (NLP) models like FinBERT and GPT-4 with time-tested machine learning procedures such as Logistic Regression. The hybridization yields predictability enhancement, resulting in a greater understanding of market sentiments through textual data analysis of sources such as news articles and social media. The research stresses the applicability of the training of market language models in assessing nontrivial financial data, illustrating how sentiment analysis could largely contribute towards stock price prediction. Then, there is comparative analysis between the models-the strengths and limitations of various AI approaches; hence, some future work ideas and innovations are suggested in areas of AI-based analytics for financial issues. Research Limitations Research into the effects of sentiment analysis and AI on forecasting stock price in future yields many limitations. The first constraint is the fact that the intensity of sentiment analysis is entirely dependent on the quality and relevant noise and biases contained in textual data sources (for example, news articles and social media posts). Of course, ajik, the rapidly fluctuating nature of financial markets means the approach demands continuous resource-intensive updating with new data in order to remain of any relevance. Furthermore, the sophisticated and computationally intensive requirements of highly advanced AI architectures like FinBERT and GPT-4 can limit them by practicality for real-time predictions. Furthermore, there will be ethical considerations like the potential to manipulate markets and privacy issues that need to be addressed for responsible use of AI in financial markets. Practical Implications The practical dimension of sentiment analysis and AI being applied to stock price prediction is an area of importance not only to investors but to financial institutions as well. Whenever the investors can predict the movement of stock prices with higher accuracy, they can thus consider their options in making investment decisions, leading to a higher profit or reduced risk. On the other hand, financial institutions can learn from such insights to better develop trading strategies, optimize management portfolios, and analyze the market. Sentiment analysis can also detect trends and shifts in investor sentiment, ensuring an additional edge in this fast-paced financial terrain. Social Implications The social implications of sentiment analysis and AI in predicting stock prices assume several facets. One is that both afford potential democratization of financial insight, thus allowing the individual investor to make more informed decisions and perhaps to realize better returns. Such democratization may engender increased financial inclusion and empowerment. On the flip side, worries about market manipulation and the legit use of AI arise. Advanced algorithms could easily be leveraged to manipulate stock prices. Then again, dependence on AI and sentiment analysis might cause a job loss in conventional financial analysts, which means advanced training of the workforce. These technologies have a lot of good to offer. With that comes the responsibility of considering their social ramifications.