The fusion of the project is a hybrid algorithm combining the FP-Growth and ECLAT methods to improve the efficiency and scalability of market basket analysis. Furthermore, this research will extend the hybrid model by integrating a neural network trained on the identified frequent itemsets to uncover underlying patterns and enhance predictive accuracy. Traditional methods often face limitations when dealing with large transactional datasets, including high computational costs and memory requirements. By leveraging the hierarchical FP-tree structure of FP-Growth, the vertical tid-list intersection strategy of ECLAT, and the pattern recognition capabilities of neural networks, this hybrid model aims to streamline frequent itemset mining while ensuring high accuracy. Experimental evaluations on benchmark datasets show that the proposed hybrid algorithm, augmented with neural network learning, outperforms standalone approaches, offering faster execution times, reduced memory usage, and improved predictive performance. This research contributes a robust framework for large-scale market basket analysis, applicable in retail and e-commerce settings.