Most contemporary parking space allocation systems encounter challenges in accurately identifying available spaces due to the constraints imposed by fixed camera angles. In this article, the Polyline Function approach is presented and implemented, an innovative methodology aimed at improving the accuracy of parking space detection and license plate recognition through the utilization of YOLOv8 in conjunction with customizable parking space delineation via OpenCV. This system empowers parking space allocators to define parking spaces with flexibility by employing polylines, thereby mitigating the limitations associated with immobile camera configurations. The proposed framework, designated as Polyline Parking Allocation Architecture, enhances both the accuracy and efficiency of detection in real-time surveillance and timestamp-based vehicle billing. This methodology offers a stable, flexible, and precise solution for parking management, optimizing spatial utilization and elevating overall operational efficiency. The matrices used in this paper, which include Precision, Recall, and F1-score, demonstrate how improved accuracy metrics contribute to enhanced detection performance. Simulation results further validate the robustness of the proposed model, showcasing its superior performance compared to earlier approaches.