Potassium is one of the metal ions which could replace lithium in energy storage devices. K is more abundant compared to Li sources and hence reduces the production cost. K-ion batteries have a similar rocking chair mechanism like LIBs. K + having large atomic radius (1.38 Å) has a small Stokes radius in various organic electrolytes which results in higher ionic conductivity. Though the electrode materials for LIBs have been extensively explored, but the same is not true for K-ion batteries. However, seeking suitable electrode materials for K ion battery is experimentally challenging and even theoretically, requires high computational facilities. Majority of the electrode materials used for LIBs are still unexplored for K-ion battery due to the difficulties in experimental and computational screening of large number of electrode materials with high accuracy. Therefore, machine learning (ML) could be an advanced tool which can save both time and cost, and at the same time screen many electrodes with minimum computational cost. For the exploration of electrode materials by ML, verified and well-curated data is needed, which is enabled by the density functional theory (DFT) based databases like NOMAD, OQMD, AFLOW, Materials Project. Although use of DFT based data is not standard for every context, still it delivers sensible insights which ultimately helps in the guidance of experimental research. ML combined with data from various databases can be used for predicting any specific property of interest for a particular battery material. Application of ML in the field of material science can be found in the prediction of microscopic properties like band structure, formation energy, solar cells, batteries, density of states and catalysis. Kernel ridge regression (KRR) and support vector regression (SVR) has been used by Seko et al. for the prediction of thermal conductivity and cohesive energy of binary and tertiary compounds. ML techniques have also been used for the prediction of different properties for their applicability as materials in photovoltaic cells and glass alloys. Sendek et al., used the logistic regression for proposing 12000 Li containing solids as solid-state electrolytes for LIBs by rapid screening. Meredig et al. built ML model to estimate thermodynamic stability and proposed around 4500 stable novel materials. ML has also been used for the improvement of DFT , prediction of thermal, electronic and mechanical properties. Capacity is one of the important metrics for the measurements of battery performance. The longevity of a battery mainly depends on cycle life of a battery and the former directly related to the capacity of a battery. From the number of ions intercalated in an electrode material we can find out the capacity of that electrode material and in order to do so quantum mechanically we need to perform DFT calculations for each individual electrode material which is very time-consuming process. However, we can utilize the different advance machine learning model as a tool to speed up the screening of electrode materials based on capacity as target variable. As far best of our knowledge till now there are very few studies has been carried out on the capacity using ML via cycle life for a particular electrode material. However, there are hardly any work has done directly on capacity of different electrode materials by ML. In this study we have utilized the Li, Na, and K ion battery data for the training of ML models in order to predict the capacity of those electrode materials for the K ion battery. The capacity of different electrode materials varies rapidly, and the range of minimum capacity and maximum capacity is very high. Keeping that in mind, we have only considered the monovalent ions and not bivalent and trivalent ions for intercalation. We have also not considered the lower alkaline metal ions since the radius of those ions will increase as we go down the group. Among the metal ion batteries, LIBs have been explored extensively, however, experimentally or by DFT calculation testing all those LIBs electrode materials for K ion batteries is a lengthy process. Therefore, after considering the Li, Na and K ion battery data for the training set we have replaced the Li and Na by K for an approx. estimation of capacity with the help of different machine learning models. Here, we have used Support vector machine (SVM), ExtraTrees regressor (EXR) and kernel ridge regression (KRR) to fit training dataset. With addition to our particular interest that is capacity, we further used the predicted capacity for the calculation of number of K ion could be intercalated in the LIBs and NaIBs electrode materials. The performance of different ML model was assessed by mean absolute percentage error (MAPE). DFT calculation for few unknown electrode materials has been performed to validate the machine learning model.