AI and ML have impacted industries and research areas, resulting in huge academic interest. In this study, we provide a deep exploration of the evolution of computational intelligence through research trends, prominent challenges and future research opportunities. By applying bibliometric methods to a large dataset of academic articles, we uncover emerging themes, prolific authors, and patterns of collaboration. We are analyzing the next few paragraphs based on the growth and potential areas of interest in future for AI/ML research which the data indicates there are deep learning, natural language processing and explainable AI. Yet, despite these breakthroughs, ethical issues, algorithmic biases, and high computational power requirements remain impediments. Promising advances in quantum AI, neuromorphic computing, and autonomous systems are expected to guide the future landscape of computational intelligence. The selected Scopus data set of 148 documents exhibits the annual growth rate of 11.61% and mean citation of 11per documents with Author participation number of 529. By mapping out these insights, this study offers valuable guidance to researchers and professionals navigating the ever-evolving landscape of AI and ML. Most influential source is Studies in Computational Intelligence and year 2024 stands out as most remarkable year for annual scientific production.