Implementing layers in TensorFlow.js involves understanding layer types and their interaction with data. Key components include convolutional layers for image processing, LSTM layers for sequential data, and dropout layers to prevent overfitting. Choosing appropriate activation functions is crucial for model performance, especially in classification tasks.