This study provides a structured analysis of modern methods used for content filtering in computer systems, with attention to their efficiency, computational demands, scalability, and implementation feasibility. The review outlines both the strengths and weaknesses of widely adopted strategies, while also assessing their applicability amid rising data volumes and emerging digital threats. The research discusses four key filtering approaches: URL blacklists, keyword detection, machine learning solutions, and semantic context analysis. Drawing on recent academic literature, the article organizes the core attributes of each method into a comparative table based on accuracy, adaptability, complexity, and usage scope. The findings confirm that none of these methods is fully comprehensive, as each comes with its own trade-offs. As a result, the article supports the concept of a layered filtering framework that integrates the strengths of various methods. This structure is composed of four stages of content handling — from initial rule-based filtering to deeper semantic interpretation. The developed model offers a well-balanced synergy between filtering precision, system responsiveness, and rational use of computational power. It remains adaptable to emerging threats within rapidly changing digital ecosystems. Future research directions may include reducing false positive detections, improving computational efficiency, and advancing the integration of AI-based solutions and linguistic analysis technologies to strengthen filtering intelligence. The implementation of such systems will contribute to the advancement of modern digital infrastructures that are scalable, intelligent, and secure, especially in the context of computer science.

smart content filtering, machine learning techniques, adaptive computing architectures

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