When it comes to the geosynthetic-reinforced soil structures’ structural design, the analysis of deformation is of the highest relevance. In spite of this, the academic literature lays a substantial amount of attention on the potential of artificial intelligence approaches in efficiently addressing the many challenges that are faced in geotechnical engineering. The investigation of the possible use of approaches based on machine learning for the purpose of forecasting the geogrid-reinforced soil structures’ deformation ($${\text{Dis}}$$
Dis
) was the major focus of this study. These efforts are made to reduce the time and cost of numerical modeling. This study aimed to enable computers to learn patterns and insights from data to make accurate predictions or decisions about unseen data. This paper introduces novel systems that coupled the Beluga whale optimizer ($${\text{BWH}}$$
BWH
), Henry gas solubility optimization ($${\text{HGSO}}$$
HGSO
), gannet optimization algorithm ($${\text{GOA}}$$
GOA
), and Harris hawks optimizer ($${\text{HHO}}$$
HHO
) with adaptive neuro-fuzzy inference system ($${\text{ANFIS}}$$
ANFIS
). A dataset was created by gathering 166 finite element analyses accomplished in the literature. Between four $${\text{ANFIS}}$$
ANFIS
systems, the integrated one with $${\text{GOA}}$$
GOA
got the largest accuracy value, accounting for 0.9841 and 0.9895 in the train and test stages, better than $${\text{ANF}}_{{{\text{BWH}}}}$$
ANF
BWH
, followed by $${\text{ANF}}_{{{\text{HH}}}}$$
ANF
HH
. It was seen from $${\text{U}}_{{{95}}} { }$$
U
95
that the $${\text{ANF}}_{{{\text{GOA}}}} { }$$
ANF
GOA
scenario exhibits the least level of uncertainty in comparison to other models, hence demonstrating its greater capacity for generalization. Between four $${\text{ANFIS}}$$
ANFIS
systems, the most accurate system with the lowest $${\text{OBJ}}$$
OBJ
value is $${\text{ANF}}_{{{\text{GOA}}}}$$
ANF
GOA
at 2.6098, followed by $${\text{ANF}}_{{{\text{BWH}}}}$$
ANF
BWH
at 2.8002. Sensitivity analysis depicts that removing the surcharge $$\left( q \right)$$
q
parameter from the input group has a considerable negative impact on output accuracy.
