The rising use of unmanned aerial vehicles (UAVs) has introduced benefits across industries and significant threats like espionage, smuggling, and airspace violations, necessitating effective anti-drone solutions. This study explores acoustic, RF, radar, vision-based, and multi-modal approaches for drone detection, each with unique strengths and challenges. Multi-modal sensor fusion addresses individual limitations, enhancing detection accuracy, robustness, and real-time tracking. Advanced machine learning algorithms and diverse datasets further improve system reliability, tackling issues like environmental interference and stealth drone detection. This work provides a deep study for scalable and efficient anti-drone systems, ensuring public safety and securing critical infrastructure in evolving UAV scenarios.