The volume and quality of online data has increased tremendously. Retrieval of such data relies so much on efficient methods. In recent times, information retrieval looks to the intelligence-based and inductive learning methods, such as genetic algorithm, neural networks and machine learning. Researchers however, have leverage on these newer techniques in order to enhance the retrieval capabilities and information processing of current information storage and retrieval systems. These methods provide various degrees of accuracy. But how effective are these methods and which of them is better suited for the information retrieval task? This paper investigates the efficiency of the selected algorithms: Artificial Neural Network, Support vector machine, and Genetic Algorithm, on designing the model for efficient and intelligent information retrieval. The selected algorithms were critically studied in line with the available matching models for information retrieval. Models like the Vector space model, Binary model, probabilistic models, Inverted Index, Latent semantic Analysis and the Latent Semantic Index models were respectively examined. The result from the experimentation from the respective algorithms shows that the neural network, in combination with Genetic algorithm or alone, performs better. However, it takes more time to execute.