This study utilizes geotagged microphone technology to analyze vehicular mobility patterns and noise pollution in Indian suburban regions. Audio data capturing traffic scenarios were meticulously calibrated using a Sound Pressure Level Meter (SPLM) and manual markers to identify five mobility types. A novel approach employing a modified Convolutional Neural Network (CNN) was developed to accurately extract vehicles and characterize mobility patterns, achieving an impressive 92.1% accuracy rate. Particularly it shows significant performance in identifying light mobility events (95% accuracy) and honking events (65% accuracy), surpassing conventional method gaining knowledge of and deep learning strategies. Integrating the modified CNN into the pipeline superposed recognition technique by 4.12%. The version gives special insights into vehicular speed and noise characteristics, facilitating specific detection of mobility patterns. The advent of mobility maps contributes to Earth Science Informatics by using integrating geospatial analysis with advanced neural network strategies, imparting a comprehensive information of vehicular mobility and noise dynamics in Indian suburban environments.