Oryza sativa, commonly known as rice, is a widely studied model organism in plant biology. Understanding the subcellular localization of proteins within rice cells is crucial for unraveling their functions and deciphering complex cellular processes. Subcellular localization provides valuable insights into protein trafficking, interactions, and regulatory mechanisms, ultimately aiding in the identification of potential targets for agricultural and biotechnological applications. Advancements in high-throughput sequencing technologies have led to an exponential increase in available genomic and proteomic data. However, experimental determination of protein localization remains labor-intensive and time-consuming. Consequently, computational methods that can accurately predict protein subcellular localization have gained significant attention. RSLpred-2.0 deep neural network for Oryza sativa subcellular localization prediction. The RSLpred-2.0 model utilizes a multi-layered architecture with a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). It takes protein sequence inputs and generates predictions for localization to various cellular compartments, including the nucleus, cytoplasm, plasma membrane, endoplasmic reticulum, Golgi apparatus, mitochondria, peroxisomes, and vacuoles. RSLpred-2.0 is implemented in four levels to accurately predict protein subcellular localization. The first level differentiates between single and dual localization with accuracy (97.66% in 5-fold training/testing, 98.12% on independent data) and Matthews correlation coefficient (0.88 training, 0.90 independent). Single localized proteins are classified into ten classes at the second level, with accuracy (98.33% in 5-fold training/testing, 98.46% on independent data) and Matthews correlation coefficient (0.95 training, 0.95 independent). The third level categorizes dual localized proteins into six classes with accuracy (99.20% in 5-fold training/testing, 96.75% on independent data) and Matthews correlation coefficient (0.98 training, 0.90 independent). The fourth level classifies membrane proteins predicted in level 1 into single-pass and multi-pass membranes with accuracy (99.83% in 5-fold training/testing, 98.81% on independent data) and Matthews correlation coefficient (0.99 training, 0.97 independent). Accurate subcellular localization prediction is crucial for unraveling protein functions and biological processes in plants. The RSLpred-2.0 deep neural network provides a powerful tool for the Rice research community, aiding in the efficient annotation and characterization of protein localization, thereby accelerating our understanding of plant cellular biology.