We investigate the estimation of a density f from n-sample on an Euclidean space RD, when data are supported by unknown submanifold M possibly dimension d<D, under reach condition. several nonparametric kernel methods, with data-driven bandwidths that incorporate some learning geometry via local estimator. When has Hölder smoothness ? and regularity ?, our estimator achieves rate n?????(2???+d)...