The exponential growth of sophisticated cyber threats has outpaced traditional defense mechanisms, necessitating the integration of adaptive and intelligent approaches in intrusion prevention systems (IPS). Conventional IPS models, primarily dependent on static rule-based or signature-based methods, often fail to detect novel or evolving attack vectors. In this context, self-learning algorithms have emerged as a transformative force capable of enhancing intrusion prevention accuracy through dynamic learning, adaptive modeling, and real-time decision-making. By leveraging techniques such as supervised, unsupervised, and reinforcement learning, these algorithms enable systems to continuously analyze network traffic, identify anomalous behaviors, and refine detection strategies without explicit human intervention. This adaptability ensures the timely recognition of zero-day exploits, polymorphic malware, and advanced persistent threats that evade traditional mechanisms. Furthermore, self-learning models contribute to the reduction of false positives and operational overhead by intelligently distinguishing between benign and malicious activities based on contextual understanding. Recent advancements in deep learning architectures, hybrid learning frameworks, and federated intelligence have further strengthened the scalability and responsiveness of IPS in cloud and distributed network environments. Despite their immense potential, challenges related to data imbalance, model transparency, adversarial manipulation, and computational demand continue to hinder widespread adoption. This review critically examines the influence of self-learning algorithms on improving intrusion prevention accuracy, exploring their methodological foundations, performance outcomes, and limitations. It also highlights the trajectory of future research focusing on autonomous, interpretable, and resilient AI-driven cybersecurity systems that combine predictive intelligence with operational efficiency. The findings underscore that the integration of self-learning algorithms represents a paradigm shift from reactive detection to proactive, context-aware defense mechanisms, setting the foundation for intelligent, self-evolving network protection architectures.