Autonomous intelligent systems have emerged as one of the most transformative technological paradigms of the twenty-first century, reshaping domains ranging from mobile robotics and unmanned aerial vehicles to autonomous driving, healthcare automation, and Industry 4.0 ecosystems. At the heart of these systems lies the problem of decision-making under uncertainty, where path planning and navigation represent foundational challenges. Among the diverse optimization paradigms proposed to address these challenges, Particle Swarm Optimization (PSO) and its numerous improved, hybrid, and multi-objective variants have demonstrated remarkable adaptability, scalability, and conceptual simplicity. This research article presents an extensive, theory-driven, and application-oriented synthesis of PSO-based path planning methodologies within the broader context of autonomous systems and artificial intelligence. Drawing strictly on the provided references, the article explores the evolution of PSO techniques, including adaptive weighted PSO, quantum-behaved PSO, crisscross learning strategies, fuzzy-enhanced multi-objective PSO, and safety-aware spherical vector-based PSO, and examines their deployment across mobile robots, unmanned aerial vehicles, unmanned surface vehicles, and autonomous driving systems. Beyond algorithmic mechanics, the study situates PSO-driven path planning within socio-technical systems, highlighting its implications for safety assurance, explainability, human–machine interaction, and ethical deployment. The methodology adopted is qualitative and integrative, relying on deep theoretical elaboration, cross-domain synthesis, and comparative reasoning rather than numerical experimentation. The results demonstrate that PSO-based approaches provide a unifying optimization framework capable of accommodating multi-objective trade-offs, environmental uncertainty, and dynamic constraints, while remaining extensible through hybridization with fuzzy logic, evolutionary learning, and safety metrics. The discussion critically evaluates limitations related to convergence reliability, explainability, and real-world validation, and outlines future research directions that emphasize interpretable autonomy, safety-critical verification, and cross-sectoral integration. The article concludes that PSO-based path planning is not merely an algorithmic solution but a conceptual bridge linking optimization theory, autonomous intelligence, and socio-technical trust in intelligent systems.