Adaptive Fusion and Transfer Learning for Enhanced E-commerce Recommendations (AFETLER) is a holistic combination of some state-of-the-art recommendation frameworks. Most of the existing frameworks, be it Matrix Factorisation model, Deep learning-based model, Factorisation Machines and Two tower recommendation models, used in real world recommendation engines lack diversity and serendipity. Also, cold start problem is one that downgrade the recommendation algorithm. Defining the optimisation goal and closed feedback improvement loop is also something that is not given primary weightage.To overcome the above stated challenges and provide a novel approach to overcome those, we propose the AFETLER model. AFETLER incorporates an adaptive fusion technique that dynamically combines recommendations from several sources such as new arrivals forecast, Click-Through Rate (CTR) prediction, and popularity estimation. This fusion technique improves accuracy by giving varying weights to each source based on the user's context, history preferences, and current behaviour, resulting in highly relevant and tailored buying recommendations. To address the cold-start problem and improve recommendations for new items, AFETLER employs a transfer learning mechanism inspired by the Adversarial Two-tower Neural Network (ATNN) framework, utilizing knowledge from existing items' interactions to extract valuable features from new items' profiles. Furthermore, AFETLER leverages hierarchical attention methods to effectively capture user-item interaction patterns, boosting feature extraction and overall recommendation quality through dynamic weighing of interaction relevance.Proposed AFETLER model outperforms some of the existing recommendation models by an average 2%-5% on metrics such a precision, ROC-AUC, and Top-K recommendation accuracy. Further, we also see point reduction in Mean absolute error and mean squared error.