This study presents the development of an improved state of charge (SOC) estimation technique for lithium–sulphur (Li–S) batteries. This is a promising technology with advantages in comparison with the existing lithium-ion (Li-ion) batteries such as lower production cost and higher energy density. In this study, a state-of-the-art Li–S prototype cell is subjected to experimental tests, which are carried out to replicate real-life duty cycles. A system identification technique is then used on the experimental test results to parameterize an equivalent circuit model for the Li–S cell. The identification results demonstrate unique features of the cell’s voltage-SOC and ohmic resistance-SOC curves, in which a large flat region is observed in the middle SOC range. Due to this, voltage and resistance parameters are not sufficient to accurately estimate SOC under various initial conditions. To solve this problem, a forgetting factor recursive least squares (FFRLS) identification technique is used, yielding four parameters which are then used to train an adaptive neuro-fuzzy inference system (ANFIS). The Sugeno-type fuzzy system features four inputs and one output (SOC), totalling 375 rules. Each of the inputs features Gaussian-type membership functions while the output is of a linear type. This network is then combined with the coulomb-counting method to obtain a hybrid estimator that can accurately estimate SOC for a Li–S cell under various conditions with a maximum error of 1.64%, which outperforms the existing methods of Li–S battery SOC estimation.