Maintenance of performance, reliability, and efficiency of any system within distributed computing environments is central to QoS. Conventional QoS optimization methods confront challenges regarding dynamic resource allocation, failure, and adaptation of systems. To overcome these limitations, a hybrid artificial neural network and Ant Colony Optimization model are proposed to provide an efficient QoS optimization strategy. The ANN part predicts possible degradation in QoS through parameters like CPU utilization, memory usage, network latency, and response time, while the ACO component dynamically optimizes resource allocation for better system performance. The proposed model considers a systematic workflow comprising data collection, pre-processing, feature extraction, QoS prediction, and ACO-based optimization. The system model training and evaluation are done using the Kaggle dataset Synthetic Log Data of Distributed Systems.As a complement to the handling of missing values with outlier treatment, the fact that Principal Component Analysis (PCA) can be used for feature selection is not omitted. The ANN model establishes the QoS importance trend, which is then optimized by ACO via pheromone-based learning and heuristic value adjustments for improved resource allocation. The experimental findings present the Hybrid ANN-ACO Model with better performance results compared to the existing QoS optimization approaches such as Rule-Based, Genetic Algorithm (GA), and Probabilistic Model Checking (PMC). In this context, the new proposed model saw a performance increase in accuracy to 96.2%, improvement to 59.4% response time, 93.1% CPU utilization efficiency, and 51.2% network latency performance.MSE is less than 0.029, meaning that we have a high level of accuracy for predicting QoS. This study illustrates the successful application of machine learning in combination with bio-inspired optimization for adaptive, scalable, and efficient QoS management in distributed computing environments.
