Adversarial attacks pose a significant threat to machine learning models, particularly in applications involving critical domains such as autonomous systems, cybersecurity, and healthcare. These attacks exploit vulnerabilities in the models by introducing carefully crafted perturbations to input data, leading to incorrect predictions and system failures. This research focuses on strengthening machine learning systems by employing robust methodologies, including input normalization, randomization, outlier detection, manual dataset curation, and adversarial training. The study highlights how these strategies collectively enhance the resilience of models against adversarial manipulations, ensuring their reliability and security in real-world scenarios. Experimental evaluations demonstrate notable improvements in robustness, with attack success rates reduced significantly while maintaining high accuracy levels. The findings emphasize the importance of a comprehensive, multi-pronged approach to safeguard machine learning systems, paving the way for secure and trustworthy AI applications in dynamic environments.