The Least squares is the traditional regression technique for pricing European options in incomplete markets. However, the least squares is quite sensitive to even a single outlier in the data, and thus, the predicted option price may potentially deviate from the true unknown one. To handle the problem of outliers, this paper aims to develop two different option pricing prediction strategies mainly based on the idea of robust linear regression. The proposed robust techniques are evaluated on numerical data, and the results of which demonstrate their effectiveness for European call option pricing on exchange rates.