This paper investigates effects of the dual approach of adding nanoparticles and ethanol to palm biodiesel-diesel fuel at varied percentage as additive. Four samples namely: E10(100) B20, E15(100) B20, E10(100) B50, and E15(100) B50 were investigated on a single cylinder L70N Yanmar engine at constant speed of 2500 rpm under three load categories of 25 %, 50 % and 75 % for each sample. The experimental data was then feed-in as training data to develop an Artificial Neural Network (ANN) using the feed-forward back propagation algorithm (FFBP) and applying the Levenberg-Marquardt training pattern. Four (4) inputs were used viz: engine load, fuel, additive and cetane number. Correlation coefficient (R) and determination coefficient (R2) were used to validate performance of the network with two set of four hidden layers while targeting 5 outputs viz: BTE, HC, CO, NOx, and smoke. Experimental results indicate that at 25 % load the Brake Thermal Efficiency (BTE) values of samples E10(100) B20, E15(100) B20, E10(100) B50, and E15(100) B50 were 10.131 %, 14.972 %, 13.945 %, 14.091 % while at 50 % load it was 18.201 %, 25.101 %, 23.046 %, 23.779 % and at 75 % BTE values was 23.779 %, 32.011 % and 30.826 % which showed engine performance can be improved by magnetite nanoparticles addition with ethanol to biodiesel-diesel fuel up to 7.4 % in terms of BTE compared to only biodiesel-diesel blend fuel; emission was noticeably reduced for tested gases compared to previous literatures. The validated ANN results range 0.99350 to 0.99975 and indicated an acceptable level for the ANN prediction model for BTE. ANN exhaust emission prediction implicated smoke and NOx target performance was most accurate with R and R2 values at 0.9942, 0.9984, and 0.9980, 0.9960 respectively which indicated minimal error and hence the ANN model was satisfactory.