Employee learning motivation is vital for employee professional development and organisational success. However, worldwide statistics show that employees are generally unmotivated to learn. This study aims to examine employee learning motivation signals to determine the best-fit model for early intervention. In this paper, we present SEeMS a Smart Employee learning Motivation System to predict employee learning motivation autonomously. An Advanced Artificial Neural Networks (AANN) with a blended activation function of Sigmoid and ReLu (bSigReLu) is proposed and compared with other learning models. Experimental results demonstrate that the proposed model outperformed conventional state-of-art models. This novel study contributes to the field of organisational behaviour and data science by extending the usage of kernels and customised activation functions to solve the employee learning motivation problem. The superiority of the algorithm makes SEeMS ideal for practical deployment. According to the predictions, organisations could design better strategies to improve employee learning motivation for targeted employees. It is the first step towards achieving an eco-system of self-motivated employee learning that contributes to employee job satisfaction, performance, and well-being, indirectly contributing to employer competitiveness.