The need to efficiently and accurately mine frequent patterns from transactional data is, therefore, quite imperative for businesses desiring to understand customer behavior and market trends. Most of the traditional pattern mining algorithms, like Apriori and FP-Growth, suffer from scalability and accuracy issues in processing large and complex datasets and their samples. It addresses these limitations by coming up with an improved algorithm that makes use of machine learning techniques to provide enhancements in efficiency, scalability, and accuracy in pattern discovery. We present a model that puts together the functioning of RNNs with LSTM units, CNNs, and autoencoders. RNNs with LSTM units have been added to model long-term dependencies in sequential data and further the accuracy of pattern identification. Through their convolutional layers, CNNs are used in the detection of local patterns. This brings about efficiency and scalability in parallel processing. Dimensionality reduction is done on transactional data using autoencoders, which in turn makes the key patterns easier to extract, and further improves efficiency in processing. In the pattern extraction phase, the FP-Growth algorithm constructs an FP-tree for the identification of frequent itemsets, whereas the Apriori algorithm iteratively goes through candidate itemsets to make sure that only very relevant and most frequent patterns are extracted. Association rule mining, in this way, generates relationships between items that are then evaluated by support, confidence, and lift measures to optimize such patterns. Genetic algorithms simulate a process of natural selection that evolves candidate solutions to guarantee the optimization of patterns for the most valuable insights in the process. This approach offers overall relief not only from the limitations of the existing algorithms but also garners significant benefits in dealing with large datasets, improved efficiency, scalability, and accuracy. The optimal patterns extracted from them give very deep insight into customer buying behavior and market trends, hence help in data-driven decision-making processes. It is the hybridization of the state-of-the-art machine learning models with traditional pattern mining techniques that is the latest development in this field and it foretells its impactful applications in all types of business genres.