Information Retrieval (IR) systems have undergone significant changes due to the rapid growth of digital information, artificial intelligence, cloud computing, social media, and mobile technologies. Current trends in IR focus on making information access more intelligent, personalized, interactive, and efficient. At the same time, research in IR is addressing new challenges created by the enormous volume, variety, and complexity of digital information. One important current trend is the use of Artificial Intelligence and Machine Learning in IR. Traditional retrieval systems mainly depended on manually designed rules, keywords, and statistical techniques. Modern systems increasingly use machine learning and deep learning to understand queries, documents, user behaviour, and relevance. Neural networks and transformer-based language models can identify semantic relationships between words and retrieve information even when the exact query terms are not present. Another major trend is semantic search. Conventional keyword-based retrieval may fail when users express the same concept using different words. Semantic IR attempts to understand the meaning and context of queries and documents. Techniques such as word embeddings, knowledge graphs, natural language processing, and large language models are