The stock market is widely recognised for its inherent volatility and complexity, absence of a clear trend, presenting an ongoing challenge for traders, investors, hedge fund managers, and portfolio management services in predicting the unpredictable. The audacious use of Artificial Intelligence (AI) in the financial sector has transformed stock market investing, stock market prediction and trading practices. Artificial intelligence possesses the capacity to transform stock prediction by utilising its capability to analyse and comprehend extensive data sets with a speed and scale that exceed human proficiency.

This study tries to elucidate the AI Methodologies, techniques, measures and informational sources used in stock market prediction. It deals with the detailed bibliometric analysis using published data from the Elsevier Scopus database to investigate the development and application trends of artificial intelligence in stock market forecasting. The search query "TITLE-ABS-KEY"("Artificial intelligence" AND "stock market prediction" OR "Financial forecasting") was utilised to identify relevant publications on the application of artificial intelligence in stock market forecasting. It aimed to analyse publishing patterns & identify main contributors, prolific authors, significant institutions, leading countries, the temporal evolution of publications, citation trends, and the distribution of research across various sources.

The findings of the study show that the highest level of number of publications is noted between 2021 and 2022 approximately equal to 40. The journal named Knowledge-Based Systems has maximum publications, surpassing 35, followed by Decision Support Systems with around 20 publications. The International Journal of Forecasting has over 3500 citations, making it the most