Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3680 2018 2018 PhD Scholar, GITAM Deemed to be University, Vishakhapatnam, India; Assistant Professor, Department of Computer Science and Engineering, ANITs, Vishakhapatnam, India Badana Badana Assistant Professor, Department of Computer Science and Engineering, GITAM deemed to be University, Vishakhapatnam, India Mandava Kranthi Kiran Precise assessment of software development effort (SDE) is essential for efficient project planning and resource distribution. Conventional methods frequently encounter difficulties in generalizing across different project areas because of disparate data attributes. This research presents an innovative approach that combines transfer learning with hybrid deep learning models to tackle these difficulties. The platform utilizes pre-trained Random Forest and LSTM models, enhanced using Jaya optimization, to improve prediction accuracy and adapt effectively to new datasets. Transfer learning is utilized to extract reusable patterns and features from source domains, facilitating effortless adaption to target domains with minimum retraining. Extensive experiments on various benchmark datasets illustrate the proposed framework's enhanced performance regarding accuracy, scalability, and robustness relative to leading techniques. This study emphasizes the capability of transfer learning to transform SDE estimates, providing a scalable and domain-adaptive approach for intricate software projects. 2025 2025 304 314 10.54216/FPA.190222 https://www.americaspg.com/articleinfo/3/show/3680