Analysis of website visitor for improving the performance of user experience on web portal is primary requirement in this online world. Hence number of web-based feature was used for the study of user behavior and recommend page accordingly. The problem is we don't have any sophisticated method to predict the pages with greater precision value in association with the faster accessing speed of the webpages this research work presents the utilization of weblog feature for analysis and pattern learning. Proposed model extract association rule forms the weblog with text pattern format on the web page. Here we have considered the input data set from a live website's weblogs called as project tunnel.com. As an output the model gives the probability of pages predicted in a sequence manner, thus increasing the precision values of predicted pages and also it increases the chromosome page quality involving a minimal value of fitness function with the regression value. The proposed work has concluded with having increasing precision value with minimal fitness value of a precision value when to compare with existing methods like Genetic feed forward association and Particle swam optimization. Association rule and text URL patterns were used for learning multi-Regression probability generation. Once the regression value is set than Intelligent Water Drop Algorithm will be applied for predicting or recommending the next page as per user previous set of pages, where IWD algorithm increases the accuracy of prediction by utilizing the regression values in fitness function. Live websites weblogs were analyzed for the experiment and comparison of proposed WPPIWD (Web Page Prediction Intelligent Water Drop) has improved the work efficiency when compared to other existing methods. Results shows that proposed intelligent water drop based Web Page Prediction Model (IWD) for next page prediction system has improved various evaluation parameters like precision, coverage, metric.