In modern digital grid systems, ensuring the stability of power operations and the accuracy of energy metering is critically dependent on effective asset identification, reconfiguration, and automated maintenance strategies. Conventional grid monitoring mechanisms like manual inspections and fixed-sensor networks also experience many difficulties, such as high costs in operation, scalability problems, and inability to present real-time, all-around information. To address these challenges, this research introduces an advanced methodology that leverages deep learning models for asset identification and automation in digital grid systems. Specifically, the research utilizes Roach Infestation Optimized Attention-based Bidirectional Long Short-Term Memory (RI-Att-BiLSTM) networks for intelligent asset identification and topology recognition in low-voltage distribution network substations. Time-series data is collected from smart meters and sensors distributed across the low-voltage distribution network. These sensors continuously capture data on electricity consumption from substations and connected consumers. The collected data is preprocessed to remove noise, outliers, and gaps, followed by normalization to scale the data for effective modeling. The RI-Att-BiLSTM-based technique reduces the dimensionality of the electricity consumption data matrix, transforming the complex topology identification task into a solvable convex optimization problem. Experimental results demonstrate that, compared to traditional methods, the proposed model achieve a superior balance between accuracy (98.43%) and higher recall, precision, and F1-score. The enhanced grid resilience provided by this methodology facilitates precise asset identification, real-time monitoring, and faulttolerant operations, thereby contributing to more efficient and reliable digital grid systems.