<p>Spoken Language Identification (SPLID) is crucial for applications like speaker recognition, automatic voice recognition, and multilingual content indexing. Recent advances in deep learning have significantly improved SPLID systems. Multi-Task Learning (MTL) is a successful paradigm that enables a model to learn multiple tasks simultaneously by using shared representations. In MTL, auxiliary tasks provide additional monitoring to support the main work during training. These supplementary activities could be related tasks, which share characteristics with the main objective, or orthogonal tasks, which are unrelated but complementary tasks that enhance the model’s ability to generalize. This work investigates the integration of orthogonal and related auxiliary tasks for spoken language identification of Indian languages within a multi-task learning (MTL) framework. We extend our previous work on related-task learning by using language family classification as the related auxiliary task and real/spoof detection as an orthogonal auxiliary task. The unified integration of both task types within a single MTL framework, which allows the model to learn complementary representations, is what makes the suggested method novel. This design improves robustness and generalization, especially in low-resource Indian language scenarios where phonetic similarities and data scarcity pose major challenges. Three SPLID datasets of Indian languages which pose difficulties because of their phonetic similarity and comparatively low resource availability were used for the experiments. A baseline model for Single-Task Learning (STL) is used to assess performance. The suggested MTL approach outperforms the STL baseline by about 7%, achieving 96.43% accuracy when orthogonal auxiliary tasks are used and 99% accuracy when both related and orthogonal tasks are integrated on the IIT-M dataset.</p>