This systematic review and meta-analysis focuses on Deep Deterministic Policy Gradient (DDPG), a popular Deep Reinforcement Learning (DRL) algorithm. The study aims to provide a comprehensive examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published between 2018 and 2023. The review covers the key concepts and components of DDPG, including its formulation, implementation, and training. It highlights various applications and domains where DDPG has been applied, such as Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. The review also provides an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, discussing its strengths and weaknesses. The study concludes with insights into future research directions, including improving sample efficiency, incorporating multiple objectives, enhancing robustness to environmental changes, and conducting theoretical analysis of DDPG. The review is intended to serve as a valuable resource for researchers and practitioners in the field of DRL and DDPG.This systematic review and meta-analysis focuses on Deep Deterministic Policy Gradient (DDPG), a popular Deep Reinforcement Learning (DRL) algorithm. The study aims to provide a comprehensive examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published between 2018 and 2023. The review covers the key concepts and components of DDPG, including its formulation, implementation, and training. It highlights various applications and domains where DDPG has been applied, such as Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. The review also provides an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, discussing its strengths and weaknesses. The study concludes with insights into future research directions, including improving sample efficiency, incorporating multiple objectives, enhancing robustness to environmental changes, and conducting theoretical analysis of DDPG. The review is intended to serve as a valuable resource for researchers and practitioners in the field of DRL and DDPG.