The subcellular localization of messenger RNAs (mRNAs) plays a crucial role in gene regulation, ensuring precise spatial and temporal control of protein synthesis. Traditional computational approaches for mRNA localization have primarily relied on single-label classification models, which fail to capture the inherent multi-label nature of mRNA localization. Recent advancements have introduced deep learning-based multi-label prediction frameworks; however, existing methods often lack an effective way to model the relationships between multiple localizations. In this paper, we propose Localization with Supervised Contrastive Learning (LOCAS), a novel approach for multi-label mRNA subcellular localization prediction. LOCAS integrates an RNA language model (RiNALMo) to generate high-quality sequence embeddings and employs supervised contrastive learning (SCL) to refine the embedding space, ensuring biologically meaningful clustering of RNA sequences. To handle overlapping labels, we introduce an overlap-threshold-based similarity measure during contrastive training. Finally, we leverage an ML-Decoder, which utilizes a cross-attention mechanism to enhance multi-label classification performance. We evaluate LOCAS on two benchmark datasets, RNALocate and RNALocate V2.0, demonstrating state-of-the-art performance across all evaluation metrics. Extensive ablation studies validate the effectiveness of our approach, highlighting the contributions of contrastive learning and ML-decoder in improving multi-label classification. Our results suggest that integrating RNA sequence representation learning with SCL offers a powerful and scalable solution for mRNA localization prediction.