This paper introduces a new metaheuristic optimization algorithm called Ship Rescue Optimization (SRO), inspired by the ship maneuvering motion function and the rescue process. The SRO algorithm is designed to solve challenging optimization problems, particularly those involving delayed and immediate rescue scenarios. The algorithm is divided into exploration and exploitation phases, with the exploration phase using a first-order approximate ship maneuvering law and the exploitation phase employing various search strategies such as spiral, sector, and inward-joining circle searches. The performance of SRO is evaluated using the CEC2013 and CEC2017 test suites and three real-world engineering problems. The results show that SRO outperforms other metaheuristic algorithms in terms of convergence speed and ability to avoid local optima, demonstrating its robustness and applicability in solving complex optimization problems. The paper also discusses the algorithm's convergence behavior and provides a detailed comparison with other algorithms, highlighting its strengths in high-dimensional problems and hybrid or composite functions.This paper introduces a new metaheuristic optimization algorithm called Ship Rescue Optimization (SRO), inspired by the ship maneuvering motion function and the rescue process. The SRO algorithm is designed to solve challenging optimization problems, particularly those involving delayed and immediate rescue scenarios. The algorithm is divided into exploration and exploitation phases, with the exploration phase using a first-order approximate ship maneuvering law and the exploitation phase employing various search strategies such as spiral, sector, and inward-joining circle searches. The performance of SRO is evaluated using the CEC2013 and CEC2017 test suites and three real-world engineering problems. The results show that SRO outperforms other metaheuristic algorithms in terms of convergence speed and ability to avoid local optima, demonstrating its robustness and applicability in solving complex optimization problems. The paper also discusses the algorithm's convergence behavior and provides a detailed comparison with other algorithms, highlighting its strengths in high-dimensional problems and hybrid or composite functions.