  Superconductors have been among the most fascinating substances, as the
fundamental concept of superconductivity as well as the correlation of critical
temperature and superconductive materials have been the focus of extensive
investigation since their discovery. However, superconductors at normal
temperatures have yet to be identified. Additionally, there are still many
unknown factors and gaps of understanding regarding this unique phenomenon,
particularly the connection between superconductivity and the fundamental
criteria to estimate the critical temperature. To bridge the gap, numerous
machine learning techniques have been established to estimate critical
temperatures as it is extremely challenging to determine. Furthermore, the need
for a sophisticated and feasible method for determining the temperature range
that goes beyond the scope of the standard empirical formula appears to be
strongly emphasized by various machine-learning approaches. This paper uses a
stacking machine learning approach to train itself on the complex
characteristics of superconductive materials in order to accurately predict
critical temperatures. In comparison to other previous accessible research
investigations, this model demonstrated a promising performance with an RMSE of
9.68 and an R2 score of 0.922. The findings presented here could be a viable
technique to shed new insight on the efficient implementation of the stacking
ensemble method with hyperparameter optimization (HPO).
