In this project, a novel concept of weblog mining is performed on e-Learning Website and also the concept of intelligent navigation behavior on user browser is proposed. Web Log Mining initially performs capturing of different weblog file while the user is accessing the e-Learning Website. Weblog file is saved. Log files can’t be directly used for pattern discovery process because it consists of irrelevant and inconsistent access information. Therefore there was a need to perform web log preprocessing which includes different techniques such as field extraction, data reduction, data cleaning, and data summarization. Field extraction and data reduction algorithm performs the process of separating fields from the single line of the log file. Data cleaning algorithm eliminates inconsistent or unnecessary items in the analyzed data. Data summarization generates a different summarized report and gives the graphical representation of the log that is being captured.Preprocessed information is given to pattern discovery and to the intelligent navigation module. Pattern discovery includes mining algorithm such as association rules, sequence patterns & clustering. Association rules is used to mine the data in order to obtain support and confidence for each rule which in turn describe association between the subjects. Clustering approach is used to cluster web sites users into different groups based on navigation behavior of the user. Intelligent navigation module uses a concept of sequence pattern and allows the student to have most frequently used subject at the top most lists, which allows them to have easy access to the tutorial or chapter within the subject i.e. most visited subject will be on the top most preference of the user menu list. We have also performed analysis on the web usage patterns and analyzed different access navigation pattern of the student, which in turn enhances the personalization services in e-Learning Website and makes the system much effective.