This paper focuses on an important yet overlooked problem of knowledge transfer over a wide range of machine-learning applications. It can be the primary reason why the current approaches are also characterized by inefficiency, the ability to accommodate only a limited number of tasks, and poorly scalable, especially where several related tasks are involved. To overcome these problems, the authors propose the Adaptive Knowledge Transfer Engine (AKTE). This new and rigorous architecture adapts transfer strategies to task similarity and re-estimates the real-time performance measures. The proposed AKTE integrates the usage of model-based, feature-based and instance-based approaches into a single hybrid approach with the possibility and flexibility of alterability for various tasks. Also, it has an inherent learning rate control that provides an adaptive approach to utilizing the existing generalized knowledge and submitting special data regarding particular tasks. For improving scalability, AKTE uses learnable attention, which makes it possible that although AKTE is designed to work in many tasks, it could make it smoother and thus less transfer efficient. The quantitative assessments prove that AKTE performs better than other transfer learning techniques. Our engine achieved 92% accuracy, notably greater than prior models and achieved convergence in 40% fewer iterations, 280 iterations in total. In addition, the efficiency of transferring knowledge was improved by 85% in the case of AKTE, proving the ability to implement knowledge to related tasks. The scalability was tested in accomplishing and handling up to fifty tasks that emerge and surpass existing models. In conclusion, as demonstrated through the discussion, AKTE offers a very flexible and fast approach to enhancing the knowledge transfer process and, therefore, is a very useful tool in enhancing various machine-learning processes.