Accurate identification of insect pests is crucial for effective agricultural management and prevention of crop losses. Among these pests, tephritid fruit flies significantly impact fruit and vegetable production, leading to economic losses and reduced market quality. Existing insect identification methods largely rely on manual inspection by taxonomists, which is time-consuming, error-prone, and not feasible for real-time applications in field conditions. Moreover, many existing machine learning-based approaches suffer from limited generalizability and dependence on controlled environments, restricting their practical deployment. To address these challenges, this study proposes a Modified Convolutional Neural Network (MCNN)-based approach for automated identification and classification of tephritid fruit fly species. The proposed method integrates image segmentation, feature extraction, and data augmentation techniques to enhance classification performance under varying conditions. A real-world dataset was collected using pheromone traps from multiple agricultural locations in Punjab, India, comprising four major species: Bactrocera dorsalis, Bactrocera zonata, Zeugodacus cucurbitae, and Zeugodacus tau. The MCNN model is trained using optimized hyperparameters, including learning rate, batch size, and optimizer selection, to improve robustness and accuracy. Experimental results demonstrate that the proposed model achieves an accuracy of 90%, along with improved classification capability compared to traditional approaches. The integration of real-field data and enhanced preprocessing techniques makes the proposed system suitable for practical deployment in precision agriculture. This study contributes to the development of an efficient, scalable, and automated insect identification framework that can assist farmers and agricultural experts in timely pest management.