In the current era of urbanization and increased motor vehicle proliferation, automated license plate recognition has emerged as a crucial technology for traffic management, security, and public safety. This study mainly focuses on the output implementing of the Bangladeshi Vehicle License Plate Recognition (BLPR) system, which has especially been designed to address the problems of the country, such as abnormally found license plates in environmental conditions and lack of advanced technological infrastructure. The proposed works integrate state-of-the-art deep learning techniques, especially Convolutional Neural Networks (CNNs), with specially made Optical Character Recognition (OCR) for Bangla scripts. The advanced model will be able to provide a solution with higher accuracy geolocation, especially in poor scenarios when plates are blurred, rotated, or partially obscured. In poor conditions where plates may blur, rotate, or partially obscure, advanced models can offer more accurate geolocation solutions. The methodology involved extensive data preprocessing, which included noise reduction, adaptive thresholding, and data augmentation, resulting in significant improvements in detection and recognition. Experimental evaluations produced an overall accuracy of 89% with different conditions considered for the tests. This innovation has practical applications in traffic surveillance, security enforcement, and access control, and it also establishes the foundation for future intelligent transportation systems.