aspg-3-3573-1791419298 20261008002818 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 1 Anita Venugopal Dhofar University, Sultanate of Oman Aditi Sharma Department of Computer Sc. and Engg, Symbosis Institute of Technology, Pune, India; Symbiosis International (Deemed) University, Pune, India Preetish Kakkar IEEE Senior Member, USA Daya Nand University of Houston, Victoria, Texas, USA Arvind R. Yadav E&I Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India Gaurav Kumar Ameta Department of Computer Sc. and Engg, Parul Institute of Technology, Parul University, Vadodara, India Fusion techniques on enhancing the efficiency of Long Short-Term Memory (LSTM) networks are dominating across a variety of domains. To handle sequential data while integrating from various sources is often challenging using LSTM techniques. Fusion methods that integrate different models enhances LSTM’ ability to handle complex correlations in the data. This paper examines early, late and hybrid fusion techniques. The study provides fusion approaches to enhance LSTM networks to efficiently handle complex multimodal data across self-navigating models. The findings reveal that the hybrid fusion techniques outperform traditional methods in terms of accuracy and generalization of various tasks. This paper proposes the Gated Recurrent Fusion (GRF) approach to demonstrate its performance to handle multimodal and temporal models in a supervised recurrence. The findings report 10% enhancement in terms of precision rate 2025 50 56 3573 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190105 https://www.americaspg.com/journal/3/article/3573 https://www.americaspg.com/storage/61739577892.pdf