The rapid emergence of computer technology has led to the storage of vast amounts of information in databases. The increasing popularity of electronic data has also created vast amounts of unlabeled information. The potential of extracting valuable knowledge from such digital data has created the basis for the researchers towards important research areas such as Web engineering, Data Science, Big Data Analytics etc. Web engineering is a process utilized for exploring patterns in large databases. However, finding intrinsic structures in large amounts of data becomes a distinctive challenge to organize them into meaningful groups. Many of the existing clustering algorithms are not appropriately suitable for all kinds of web applications. This prompted the present researchers to develop a machine algorithm that is more applicable and robust in real-time to get technological intelligence especially from web data sources. The authors propose an Optimal Segmentation System using a Machine Learning approach (MLOSS) with twin objectives. Initially, MLOSS performs the pre-processing step on the unstructured and semi-structured web documents to prepare efficient data representation structure for applying either supervised and unsupervised techniques. Later as a part of second objective, the proposed system emphasizes on the segment of the preprocessed web data using clustering techniques with an hybridization of the Genetic Approach, that mimic the biological evaluation process having the self-learning proficiency. To validate the performance results of the proposed framework in numerous orders of magnitude, much experimentation is done and, the results have proven this as claimed.