With the rapid growth of intelligent transportation systems and smart city infrastructure, Automatic License Plate Recognition (ALPR) has become an essential technology for traffic monitoring, law enforcement, and vehicle management. However, conventional ALPR systems often rely on fixed preprocessing and recognition pipelines, which significantly reduce performance under challenging environmental conditions such as low illumination, rain, fog, glare, and motion blur. These limitations lead to inaccurate license plate detection and recognition in real-world scenarios. To address these challenges, this paper proposes an Adaptive Context-Aware Deep Learning Framework for Multi-Condition License Plate Detection and Recognition. The proposed system introduces a context-aware architecture that dynamically adapts image processing and recognition strategies based on environmental conditions. Initially, a Context Classification Convolutional Neural Network (CC-CNN) is employed to analyze input images and identify environmental contexts such as daylight, night-time, rainy, foggy, blurred, and glare-affected scenes. Based on the detected context, a Context-Aware Enhancement Network (CAEN), implemented using a lightweight Convolutional Neural Network with an attention mechanism, enhances image quality while preserving critical license plate features.